experiment-notebooks/grid-road-env-delay-response-experiment.ipynb

{
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   "source": [
    "### Dynamic Interaction Experiment\n",
    "\n",
    "In this notebook we test the adaptability of our approach where the proposed governance variants are evaluated against 3x3 and 5x5 environment configurations for both fixed and random orientations.\n",
    "The _algorithm performance experiment_ explains all the existing modules present in this notebook in complete detail.\n",
    "The below _TL;DR Section Descriptions_ elaborates on the notebook implementation structure in short.\n",
    "\n",
    "### TL;DR Section Descriptions\n",
    "\n",
    "* [`Notebook Prerequisite Setup`](#notebook-prerequisite-setup): Installing `stable-baselines3` and related packages, plus defining all package imports\n",
    "* [`Gym Environment Implementation`](#gym-environment-implementation): Implementing grid road environment variants, plus setting up Gym Monitor logger\n",
    "* [`Governance Wrapper Layer Implementation`](#governance-wrapper-layer-implementation): Implementing reward shaping governance layer onto grid delivery environments\n",
    "* [`RL Algorithm Model Training`](#rl-algorithm-model-training): Training RL algorithms in simple CTCE setting for measuring approach efficacy, plus manually saving the log files locally"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='notebook-prerequisite-setup'></a>\n",
    "#### 0. Notebook Prerequisite Setup\n",
    "\n",
    "`In this section we first install stable-baselines3 and related packages required for Gym Monitor logging. And, then we import environment implementation, RL model training, and convergence plot related matplotlib packages.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "34ag2hg60oia",
    "outputId": "48299481-7001-4f00-e806-5d8024439f61"
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
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   "source": [
    "# installing the latest stable-baselines3 library\n",
    "# !pip install stable-baselines3\n",
    "!pip install stable-baselines3\n",
    "# ignoring the restart runtime instruction and continue with the cell execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
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    "id": "wJlrYdxwhBwb",
    "outputId": "8513f0e2-0765-45a4-cf74-44dee834a939"
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      "Building wheels for collected packages: box2d-kengz\n",
      "  Building wheel for box2d-kengz (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for box2d-kengz: filename=Box2D_kengz-2.3.3-cp37-cp37m-linux_x86_64.whl size=2050541 sha256=1768e55c640fb0f4921b0470ed302587ead8f5bd8a4f80a7b5fe4653c6e25068\n",
      "  Stored in directory: /root/.cache/pip/wheels/50/6d/6a/6ff76731fd9e8efbd1cdc6111e98b2dd0f1872184d7c28939c\n",
      "Successfully built box2d-kengz\n",
      "Installing collected packages: box2d-kengz, box2d\n",
      "Successfully installed box2d-2.3.10 box2d-kengz-2.3.3\n"
     ]
    }
   ],
   "source": [
    "# installing additional stable-baselines3 packages for plotting the agent learning outputs\n",
    "# !pip install stable-baselines3\n",
    "!pip install stable-baselines3[extra]\n",
    "!apt install swig cmake\n",
    "!pip install stable-baselines3[extra] box2d box2d-kengz\n",
    "# ignoring the restart runtime instruction and continue with the cell execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "W5RN83LAqWyB"
   },
   "outputs": [],
   "source": [
    "# OpenAI gym related import statements\n",
    "# building a simpler environment that works with stablebaselines\n",
    "import os\n",
    "import gym\n",
    "from gym import spaces\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "0DFgWujIh6L6"
   },
   "outputs": [],
   "source": [
    "# learning curve plotting realted package imports\n",
    "from gym.envs.registration import EnvSpec\n",
    "from stable_baselines3.common.monitor import Monitor\n",
    "from stable_baselines3.common.results_plotter import load_results, ts2xy\n",
    "from stable_baselines3.common.callbacks import BaseCallback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "NgUDplIF7SIt"
   },
   "outputs": [],
   "source": [
    "# import statements for 3D function plotting with matplotlib\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='gym-environment-implementation'></a>\n",
    "#### 1. Gym Environment Implementation\n",
    "\n",
    "This section implements different OpenAI gym's environment related classes for the two agent package delivery environment.\n",
    "The inline comments explain the implementation functionalities in detail for all the described environment and logger classes.\n",
    "\n",
    "**Note:** The description for all the classes are explained in the `Gym Environment Implementation` section of _algorithm performance experiment_ notebook, refer _grid-road-env-algorithm-performance-experiment.ipynb_ for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "4bfBZtEqin78"
   },
   "outputs": [],
   "source": [
    "# utility class for saving the best model function\n",
    "class SaveOnBestTrainingRewardCallback(BaseCallback):\n",
    "    \"\"\"\n",
    "    Callback for saving a model (the check is done every ``check_freq`` steps)\n",
    "    based on the training reward (in practice, we recommend using ``EvalCallback``).\n",
    "\n",
    "    :param check_freq: (int)\n",
    "    :param log_dir: (str) Path to the folder where the model will be saved.\n",
    "      It must contains the file created by the ``Monitor`` wrapper.\n",
    "    :param verbose: (int)\n",
    "    \"\"\"\n",
    "    def __init__(self, check_freq: int, log_dir: str, verbose=1):\n",
    "        super(SaveOnBestTrainingRewardCallback, self).__init__(verbose)\n",
    "        self.check_freq = check_freq\n",
    "        self.log_dir = log_dir\n",
    "        self.save_path = os.path.join(log_dir, 'best_model')\n",
    "        self.best_mean_reward = -np.inf\n",
    "\n",
    "    def _init_callback(self) -> None:\n",
    "        # create folder if needed\n",
    "        if self.save_path is not None:\n",
    "            os.makedirs(self.save_path, exist_ok=True)\n",
    "\n",
    "    def _on_step(self) -> bool:\n",
    "        if self.n_calls % self.check_freq == 0:\n",
    "          # retrieve training reward\n",
    "          x, y = ts2xy(load_results(self.log_dir), 'timesteps')\n",
    "          if len(x) > 0:\n",
    "              # mean training reward over the last 100 episodes\n",
    "              mean_reward = np.mean(y[-100:])\n",
    "              if self.verbose > 0:\n",
    "                print(f\"Num timesteps: {self.num_timesteps}\")\n",
    "                print(f\"Best mean reward: {self.best_mean_reward:.2f} - Last mean reward per episode: {mean_reward:.2f}\")\n",
    "              # new best model, you could save the agent here\n",
    "              if mean_reward > self.best_mean_reward:\n",
    "                  self.best_mean_reward = mean_reward\n",
    "                  # example for saving best model\n",
    "                  if self.verbose > 0:\n",
    "                    print(f\"Saving new best model to {self.save_path}.zip\")\n",
    "                  self.model.save(self.save_path)\n",
    "        return True\n",
    "\n",
    "# create log dir for the agent monitoring and result plotting\n",
    "log_dir = \"/tmp/gym/\"\n",
    "os.makedirs(log_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "2pPscl6L7Wr8"
   },
   "outputs": [],
   "source": [
    "# the 3x3 road network environment doesn't rotate in its simple\n",
    "# setting implementation\n",
    "# implementation of the Simple 3x3 road network for MultiAgent RL\n",
    "class SizeThreeSimpleGridRoadEnv(gym.Env):\n",
    "    # Defining the Driving Agent with name and gas values plus package flag.\n",
    "    class DriverAgent():\n",
    "        def __init__(self, name, gas, package, picked):\n",
    "            self.name = name\n",
    "            self.gas = gas\n",
    "            self.package = package\n",
    "            self.picked = picked # flag, package has been picked once by the agent\n",
    "\n",
    "    def __init__(self):\n",
    "        # super(SizeThreeSimpleGridRoadEnv, self).__init__()\n",
    "        # fefining different possible world configurations\n",
    "        self.world_one = np.array([[1, 0, 0],\n",
    "                      [3, 0, 2],\n",
    "                      [0, 0, 4]])\n",
    "        # self.world_two = np.rot90(self.world_one)\n",
    "        # self.world_three = np.rot90(self.world_two)\n",
    "        # self.world_four = np.rot90(self.world_three)\n",
    "        # Eeen the initial world configuration is defined to be different upon\n",
    "        # environment instantiation\n",
    "        # prob = random.uniform(0, 1)\n",
    "        # default value assignment below\n",
    "        self.world = self.world_one\n",
    "        # if prob > 0.25 and prob <= 0.25:\n",
    "        #     self.world = self.world_two\n",
    "        # elif prob > 0.5 and prob <= 0.75:\n",
    "        #     self.world = self.world_three\n",
    "        # elif prob > 0.75 and prob <= 1:\n",
    "        #     self.world = self.world_four\n",
    "        self.world_start = self.world # this 'world_start', if reset() is called, never gets used\n",
    "        # adding five actions for the environment.\n",
    "        # 0: up, 1: right, 2: down, 3: left,; 4: stay/pass chance,\n",
    "        # 5: drop -> for agent learning simplicity eliminating 4 & 5 actions\n",
    "        # when agent reaches at package location it automatically picks up the package\n",
    "        self.action_space = spaces.Discrete(4)\n",
    "        shape_0 = np.size(self.world_start, 0)\n",
    "        shape_1 = np.size(self.world_start, 1)\n",
    "        self.observation_space = spaces.Box(low=0,\n",
    "                                            high=4,\n",
    "                                            shape=(shape_0 + 1, shape_1),\n",
    "                                            dtype=np.int16)\n",
    "        self.reward_range = (-10, 10)\n",
    "        self.current_episode = 0\n",
    "        self.success_episode = []\n",
    "        # defining the driver agents in the environment\n",
    "        self.agent_one = self.DriverAgent(1,3,0,False) # 3 integer value, when carrying package\n",
    "        self.agent_two = self.DriverAgent(2,8,0,False) # 3 integer value, when carrying package\n",
    "        self.spec = EnvSpec(\"SizeThreeSimpleGridRoadEnv-v0\")\n",
    "\n",
    "    def reset(self):\n",
    "        # sequential game like formulation, each player agent moves one step at a time\n",
    "        # but resource specific delays are also added so that second agent waits for\n",
    "        # first agent to finish it complete movement in the environment\n",
    "        self.agent_one = self.DriverAgent(1,3,0,False) # instantiating agent 1 again\n",
    "        self.agent_two = self.DriverAgent(2,4,0,False) # instantiating agent 2 again\n",
    "        self.current_player = self.agent_one\n",
    "        # 'P' means the game is playable, 'W' means delivered, 'L' means no delivery\n",
    "        self.state = 'P'\n",
    "        self.current_step = 0\n",
    "        self.max_step = 32 # agent can choose not move as an alternate choice\n",
    "        # selecting a world at random to function with\n",
    "        # even the initial world configuration should be different\n",
    "        # prob = random.uniform(0, 1)\n",
    "        # if prob > 0.25 and prob <= 0.25:\n",
    "        #     self.world_start = self.world_two\n",
    "        # elif prob > 0.5 and prob <= 0.75:\n",
    "        #     self.world_start = self.world_three\n",
    "        # elif prob > 0.75 and prob <= 1:\n",
    "        #     self.world_start = self.world_four\n",
    "        # elif prob < 0.25:\n",
    "        #     self.world_start = self.world_one\n",
    "        self.world_start = self.world_one    \n",
    "        self.world = np.copy(self.world_start) # the self.world can be different from intial world\n",
    "        # no exploration_prize and bonus_reward as per the design\n",
    "        return self._next_observation()\n",
    "    \n",
    "    def _next_observation(self):\n",
    "        obs = self.world\n",
    "        data_to_add = [0] * np.size(self.world, 1)\n",
    "        data_to_add[0] = self.current_player.name # adding current player's label in the observation\n",
    "        obs = np.append(obs, [data_to_add], axis=0)\n",
    "        # observation sample provided below for reference:\n",
    "        # last row, represents 'data_to_add' vector representing the current agent\n",
    "        # array([[1, 0, 0],\n",
    "        #         [3, 0, 2],\n",
    "        #         [0, 0, 4],\n",
    "        #         [1, 0, 0]])\n",
    "        return obs\n",
    "\n",
    "    def _take_action(self, action):\n",
    "        # agent's name is matched to the array entries for index identification\n",
    "        # 'current_player.name' should be updated alongside the array values\n",
    "        current_pos = np.where(self.world == self.current_player.name)\n",
    "        # the current agent must have gas in it\n",
    "        if self.current_player.gas > 0:\n",
    "            if action == 0:\n",
    "                next_pos = (current_pos[0] - 1, current_pos[1]) # agent moving upwards\n",
    "\n",
    "                if next_pos[0] >= 0 and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] >= 0 and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two Agents can't be at the same place\n",
    "\n",
    "                elif next_pos[0] >= 0 and int(self.world[next_pos] == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent\n",
    "                    self.current_player.picked = True # package is picked once by the agent\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] >= 0 and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it.\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "\n",
    "\n",
    "            elif action == 1:\n",
    "                next_pos = (current_pos[0], current_pos[1] + 1)\n",
    "                limit = np.size(self.world, 1)\n",
    "\n",
    "                if next_pos[1] < limit and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] < limit and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two Agents can't be at the same place\n",
    "\n",
    "                elif next_pos[1] < limit and (int(self.world[next_pos]) == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent\n",
    "                    self.current_player.picked = True # package is picked once by the agent\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] < limit and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "\n",
    "\n",
    "            elif action == 2:\n",
    "                next_pos = (current_pos[0] + 1, current_pos[1])\n",
    "                limit = np.size(self.world, 0)\n",
    "\n",
    "                if next_pos[0] < limit and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] < limit and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two Agents can't be at the same place\n",
    "\n",
    "                elif next_pos[0] < limit and (int(self.world[next_pos]) == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent\n",
    "                    self.current_player.picked = True # package is picked once by the agent\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] < limit and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "\n",
    "            elif action == 3:\n",
    "                next_pos = (current_pos[0], current_pos[1] - 1)\n",
    "\n",
    "                if next_pos[1] >= 0 and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] >= 0 and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two Agents can't be at the same place\n",
    "\n",
    "                elif next_pos[1] >= 0 and (int(self.world[next_pos]) == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent\n",
    "                    self.current_player.picked = True # package is picked once by the agent\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] >= 0 and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "\n",
    "            # removing functionality for action 4 and 5 for environment simplicity.\n",
    "            # newly added logic based on new possible actions.\n",
    "            # elif action == 4 and self.current_player.name != 1: # passing is allowed for agent 2 only\n",
    "            #     pass # corresponding agent selects to not move at their chance\n",
    "            \n",
    "            # elif action == 5: # if agent is over the package, it has to pick it up, environment cases encoded above\n",
    "            #     # agent can choose to drop the package, if it is loaded with it\n",
    "            #     # after, dropping the package the agent should dissappear\n",
    "            #     if self.current_player.package == 3:\n",
    "            #         if self.world[current_pos] == 0:\n",
    "            #             self.world[current_pos] = 3\n",
    "            #             # agent dissappears from the grid after this drop\n",
    "            #         elif self.world[current_pos] == 4: # added as extra case, functionally possibly won't be triggered\n",
    "            #             self.world[current_pos] = self.current_player.name\n",
    "            #             self.state = 'W'\n",
    "            \n",
    "        else:\n",
    "            # player 1's gas is supposed to go empty first\n",
    "            # therefore, upon having empty gas tank player should be allowed to\n",
    "            # drop the package in the environment and disappear from the location\n",
    "            if self.current_player.package == 3:\n",
    "                self.world[current_pos] = self.current_player.package\n",
    "                # self.current_player.picked = False # package is dropped by the agent\n",
    "                # agent dissappears from the grid after this drop\n",
    "            else:\n",
    "                self.world[current_pos] = 0 # if gas is finished, agent should dissappear\n",
    "\n",
    "        # if gas is empty for both agents, the episode should stop at that instant\n",
    "        if self.agent_one.gas == 0 and self.agent_two.gas == 0:\n",
    "            self.state = 'L'\n",
    "\n",
    "\n",
    "    def step(self, action):\n",
    "        self._take_action(action)\n",
    "        self.current_step += 1\n",
    "        # uncomment the below statement out, while debugging\n",
    "        # print(self.world) \n",
    "\n",
    "        if self.state == \"W\":\n",
    "            reward = 2.5\n",
    "            done = True\n",
    "        elif self.state == 'L':\n",
    "            reward = 0\n",
    "            done = True\n",
    "        elif self.state == 'P':\n",
    "            reward = 0 # sparse reward encoding, only rewarded when episode ends\n",
    "            done = False\n",
    "\n",
    "        if self.current_step >= self.max_step:\n",
    "            print(f'New episode number {self.current_episode + 1}')\n",
    "            done = True\n",
    "        \n",
    "        # removing functionality for alternate play for environment simplicity\n",
    "        # agents object used to identify agent properties\n",
    "        if self.current_player.name == 1 and self.current_step > 6: # general condition > (2*gas)\n",
    "            self.current_player = self.agent_two\n",
    "        elif self.current_player.name == 2:\n",
    "            self.current_player = self.agent_one\n",
    "\n",
    "        # adding functionality of sequential agent interaction for environment simplicity\n",
    "        # if self.current_player.name == 1 and self.current_player.gas == 0:\n",
    "        #     self.current_player = self.agent_two\n",
    "\n",
    "        if done:\n",
    "            self.render_episode(self.state)\n",
    "            # self.reset()\n",
    "            self.current_episode += 1\n",
    "        \n",
    "        # if done == True and self.state == 'L':\n",
    "        #     self.reset()\n",
    "\n",
    "        obs = self._next_observation()\n",
    "\n",
    "        return obs, reward, done, {'state': self.state}, self.current_player\n",
    "\n",
    "    def render_episode(self, win_or_lose):\n",
    "        # storing the rendered episodes in a file\n",
    "        self.success_episode.append(\n",
    "            'Success' if win_or_lose == 'W' else 'Failure')\n",
    "        file = open('render.txt', 'a')\n",
    "        file.write('----------------------------\\n')\n",
    "        file.write(f'Episode number {self.current_episode}\\n')\n",
    "        file.write(\n",
    "            f'{self.success_episode[-1]} in {self.current_step} steps\\n')\n",
    "        file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "oB5PJb-2LJHS"
   },
   "outputs": [],
   "source": [
    "# the 3x3 road network environment which rotate randomly in its simple\n",
    "# setting implementation\n",
    "# implementation of the Simple 3x3 road network for MultiAgent RL\n",
    "class SizeThreeGridRoadEnv(gym.Env):\n",
    "    # defining the Driving Agent with name and gas values plus package flag\n",
    "    class DriverAgent():\n",
    "        def __init__(self, name, gas, package, picked):\n",
    "            self.name = name\n",
    "            self.gas = gas\n",
    "            self.package = package\n",
    "            self.picked = picked # flag, package has been picked once by the agent\n",
    "\n",
    "    def __init__(self):\n",
    "        # super(SizeThreeSimpleGridRoadEnv, self).__init__()\n",
    "        # defining different possible world configurations\n",
    "        self.world_one = np.array([[1, 0, 0],\n",
    "                      [3, 0, 2],\n",
    "                      [0, 0, 4]])\n",
    "        self.world_two = np.rot90(self.world_one)\n",
    "        self.world_three = np.rot90(self.world_two)\n",
    "        self.world_four = np.rot90(self.world_three)\n",
    "        # even the initial world configuration is defined to be different upon\n",
    "        # environment instantiation\n",
    "        prob = random.uniform(0, 1)\n",
    "        # default value assignment below\n",
    "        self.world = self.world_one\n",
    "        if prob > 0.25 and prob <= 0.25:\n",
    "            self.world = self.world_two\n",
    "        elif prob > 0.5 and prob <= 0.75:\n",
    "            self.world = self.world_three\n",
    "        elif prob > 0.75 and prob <= 1:\n",
    "            self.world = self.world_four\n",
    "        self.world_start = self.world # this 'world_start', if reset() is called, never gets used\n",
    "        # adding five actions for the environment\n",
    "        # 0: up, 1: right, 2: down, 3: left,; 4: stay/pass chance,\n",
    "        # 5: drop -> for agent learning simplicity eliminating 4 & 5 actions\n",
    "        # when agent reaches at package location it automatically picks up the package\n",
    "        self.action_space = spaces.Discrete(4)\n",
    "        shape_0 = np.size(self.world_start, 0)\n",
    "        shape_1 = np.size(self.world_start, 1)\n",
    "        self.observation_space = spaces.Box(low=0,\n",
    "                                            high=4,\n",
    "                                            shape=(shape_0 + 1, shape_1),\n",
    "                                            dtype=np.int16)\n",
    "        self.reward_range = (-10, 10)\n",
    "        self.current_episode = 0\n",
    "        self.success_episode = []\n",
    "        # defining the driver agents in the environment\n",
    "        self.agent_one = self.DriverAgent(1,3,0,False) # 3 integer value, when carrying package\n",
    "        self.agent_two = self.DriverAgent(2,8,0,False) # 3 integer value, when carrying package\n",
    "        self.spec = EnvSpec(\"SizeThreeGridRoadEnv-v0\")\n",
    "\n",
    "    def reset(self):\n",
    "        # game like formulation, each player agent moves one step at a time\n",
    "        self.agent_one = self.DriverAgent(1,3,0,False) # instantiating agent 1 again\n",
    "        self.agent_two = self.DriverAgent(2,4,0,False) # instantiating agent 2 again\n",
    "        self.current_player = self.agent_one\n",
    "        # 'P' means the game is playable, 'W' means delivered, 'L' means no delivery\n",
    "        self.state = 'P'\n",
    "        self.current_step = 0\n",
    "        self.max_step = 32 # agent can choose not move as an alternate choice\n",
    "        # selecting a world at random to function with\n",
    "        # even the initial world configuration should be different\n",
    "        prob = random.uniform(0, 1)\n",
    "        if prob > 0.25 and prob <= 0.25:\n",
    "            self.world_start = self.world_two\n",
    "        elif prob > 0.5 and prob <= 0.75:\n",
    "            self.world_start = self.world_three\n",
    "        elif prob > 0.75 and prob <= 1:\n",
    "            self.world_start = self.world_four\n",
    "        elif prob < 0.25:\n",
    "            self.world_start = self.world_one\n",
    "        self.world_start = self.world_one\n",
    "        self.world = np.copy(self.world_start) # the self.world can be different from intial world\n",
    "        # no exploration_prize and bonus_reward as per my design\n",
    "        return self._next_observation()\n",
    "    \n",
    "    def _next_observation(self):\n",
    "        obs = self.world\n",
    "        data_to_add = [0] * np.size(self.world, 1)\n",
    "        data_to_add[0] = self.current_player.name # adding current player's label in the observation\n",
    "        obs = np.append(obs, [data_to_add], axis=0)\n",
    "        # observation Sample provided below for reference:\n",
    "        # last row, represents 'data_to_add' vector\n",
    "        # array([[1, 0, 0],\n",
    "        #         [3, 0, 2],\n",
    "        #         [0, 0, 4],\n",
    "        #         [1, 0, 0]])\n",
    "        return obs\n",
    "\n",
    "    def _take_action(self, action):\n",
    "        # agent's name is matched to the array entries for index identification\n",
    "        # 'current_player.name' should be updated alongside the array values\n",
    "        current_pos = np.where(self.world == self.current_player.name)\n",
    "        # the current agent must have gas in it\n",
    "        if self.current_player.gas > 0:\n",
    "            if action == 0:\n",
    "                next_pos = (current_pos[0] - 1, current_pos[1]) # agent moving upwards\n",
    "\n",
    "                if next_pos[0] >= 0 and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] >= 0 and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two Agents can't be at the same place\n",
    "\n",
    "                elif next_pos[0] >= 0 and int(self.world[next_pos] == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent\n",
    "                    self.current_player.picked = True # package is picked once by the agent\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] >= 0 and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name # like 34 are already there, for example\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "\n",
    "\n",
    "            elif action == 1:\n",
    "                next_pos = (current_pos[0], current_pos[1] + 1)\n",
    "                limit = np.size(self.world, 1)\n",
    "\n",
    "                if next_pos[1] < limit and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] < limit and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two Agents can't be at the same place\n",
    "\n",
    "                elif next_pos[1] < limit and (int(self.world[next_pos]) == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent\n",
    "                    self.current_player.picked = True # package is picked once by the agent\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] < limit and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name # like 34 are already there, for example\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "\n",
    "\n",
    "            elif action == 2:\n",
    "                next_pos = (current_pos[0] + 1, current_pos[1])\n",
    "                limit = np.size(self.world, 0)\n",
    "\n",
    "                if next_pos[0] < limit and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] < limit and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two Agents can't be at the same place\n",
    "\n",
    "                elif next_pos[0] < limit and (int(self.world[next_pos]) == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent\n",
    "                    self.current_player.picked = True # package is picked once by the agent\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] < limit and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "\n",
    "            elif action == 3:\n",
    "                next_pos = (current_pos[0], current_pos[1] - 1)\n",
    "\n",
    "                if next_pos[1] >= 0 and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] >= 0 and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two Agents can't be at the same place\n",
    "\n",
    "                elif next_pos[1] >= 0 and (int(self.world[next_pos]) == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent\n",
    "                    self.current_player.picked = True # package is picked once by the agent\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] >= 0 and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "\n",
    "            # removing functionality for action 4 and 5 for environment simplicity\n",
    "            # newly added logic based on three new possible actions\n",
    "            # elif action == 4 and self.current_player.name != 1: # passing is allowed for agent 2 only\n",
    "            #     pass # corresponding agent selects to not move at their chance\n",
    "            \n",
    "            # elif action == 5: # if agent is over the package, it has to pick it up, environment cases encoded above\n",
    "            #     # agent can choose to drop the package, if it is loaded with it\n",
    "            #     # after, dropping the package the agent should dissappear\n",
    "            #     if self.current_player.package == 3:\n",
    "            #         if self.world[current_pos] == 0:\n",
    "            #             self.world[current_pos] = 3\n",
    "            #             # agent dissappears from the grid after this drop\n",
    "            #         elif self.world[current_pos] == 4: # added as extra case, functionally possibly won't be triggered\n",
    "            #             self.world[current_pos] = self.current_player.name\n",
    "            #             self.state = 'W'\n",
    "            \n",
    "        else:\n",
    "            # player 1's gas is supposed to go empty first\n",
    "            # therefore, upon having empty gas tank player should be allowed to\n",
    "            # drop the package in the environment and disappear from the location\n",
    "            if self.current_player.package == 3:\n",
    "                self.world[current_pos] = self.current_player.package\n",
    "                # self.current_player.picked = False # package is dropped by the agent\n",
    "                # agent dissappears from the grid after this drop\n",
    "            else:\n",
    "                self.world[current_pos] = 0 # if gas is finished, agent should dissappear\n",
    "\n",
    "        # if gas is empty for both agents, the episode should stop at that instant\n",
    "        if self.agent_one.gas == 0 and self.agent_two.gas == 0:\n",
    "            self.state = 'L'\n",
    "\n",
    "\n",
    "    def step(self, action):\n",
    "        self._take_action(action)\n",
    "        self.current_step += 1\n",
    "        # uncomment the below statement out, while debugging\n",
    "        # print(self.world) \n",
    "\n",
    "        if self.state == \"W\":\n",
    "            reward = 2.5\n",
    "            done = True\n",
    "        elif self.state == 'L':\n",
    "            reward = 0\n",
    "            done = True\n",
    "        elif self.state == 'P':\n",
    "            reward = 0 # sparse reward encoding, only rewarded when episode ends\n",
    "            done = False\n",
    "\n",
    "        if self.current_step >= self.max_step:\n",
    "            print(f'New episode number {self.current_episode + 1}')\n",
    "            done = True\n",
    "        \n",
    "        # removing functionality for alternate play for environment simplicity\n",
    "        # agents object used to identify agent properties\n",
    "        if self.current_player.name == 1 and self.current_step > 6: # general condition > (2*gas)\n",
    "            self.current_player = self.agent_two\n",
    "        elif self.current_player.name == 2:\n",
    "            self.current_player = self.agent_one\n",
    "\n",
    "        # adding functionality of sequential agent interaction for environment simplicity\n",
    "        # if self.current_player.name == 1 and self.current_player.gas == 0:\n",
    "        #     self.current_player = self.agent_two\n",
    "\n",
    "        if done:\n",
    "            self.render_episode(self.state)\n",
    "            # self.reset()\n",
    "            self.current_episode += 1\n",
    "        \n",
    "        # if done == True and self.state == 'L':\n",
    "        #     self.reset()\n",
    "\n",
    "        obs = self._next_observation()\n",
    "\n",
    "        return obs, reward, done, {'state': self.state}, self.current_player\n",
    "\n",
    "    def render_episode(self, win_or_lose):\n",
    "        # storing the rendered episodes in a file\n",
    "        self.success_episode.append(\n",
    "            'Success' if win_or_lose == 'W' else 'Failure')\n",
    "        file = open('render.txt', 'a')\n",
    "        file.write('----------------------------\\n')\n",
    "        file.write(f'Episode number {self.current_episode}\\n')\n",
    "        file.write(\n",
    "            f'{self.success_episode[-1]} in {self.current_step} steps\\n')\n",
    "        file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "dybP2J_wLJOY"
   },
   "outputs": [],
   "source": [
    "# the NxN road network environment doesn't rotate in its simple\n",
    "# setting implementation\n",
    "# implementation of the Simple NxN road network for MultiAgent RL\n",
    "class SimpleGridRoadEnv(gym.Env):\n",
    "    # defining the Driving Agent with name and gas values plus package flag\n",
    "    class DriverAgent():\n",
    "        def __init__(self, name, gas, package):\n",
    "            self.name = name\n",
    "            self.gas = gas\n",
    "            self.package = package\n",
    "\n",
    "    def __init__(self, size=5, gas=7):\n",
    "        # size: grid size, gas: values restricted between (n_min: trunc(n/2), n_max: 2*(n-1)-1)\n",
    "        # converting even size to nearest odd number\n",
    "        self.size = size\n",
    "        if size % 2 == 0:\n",
    "            self.size = self.size+1\n",
    "        # defining different possible world configurations\n",
    "        self.world_one = np.zeros((self.size,self.size), dtype=int)\n",
    "        # setting up env values for the numpy array\n",
    "        self.world_one[0][0]=1\n",
    "        self.world_one[self.size-1][self.size-1]=4\n",
    "        self.world_one[self.size//2][self.size-1]=2\n",
    "        self.world_one[self.size//2][0]=3\n",
    "        self.world = self.world_one\n",
    "        # if prob > 0.25 and prob <= 0.25:\n",
    "        #     self.world = self.world_two\n",
    "        # elif prob > 0.5 and prob <= 0.75:\n",
    "        #     self.world = self.world_three\n",
    "        # elif prob > 0.75 and prob <= 1:\n",
    "        #     self.world = self.world_four\n",
    "        self.world_start = self.world # this 'world_start', if reset() is called, never gets used\n",
    "        # Adding five actions for the environment.\n",
    "        # 0: up, 1: right, 2: down, 3: left, 4: stay/pass chance, 5: drop & dissappear\n",
    "        # when agent reaches at package location it automatically picks up the package\n",
    "        self.action_space = spaces.Discrete(4)\n",
    "        shape_0 = np.size(self.world_start, 0)\n",
    "        shape_1 = np.size(self.world_start, 1)\n",
    "        self.observation_space = spaces.Box(low=0,\n",
    "                                            high=4,\n",
    "                                            shape=(shape_0 + 1, shape_1),\n",
    "                                            dtype=np.int16)\n",
    "        self.reward_range = (-10, 10)\n",
    "        self.current_episode = 0\n",
    "        self.success_episode = []\n",
    "        # defining the driver agents in the environment\n",
    "        self.gas = gas\n",
    "        # truncating the gas to the desirable range for experimentation\n",
    "        if (self.gas < self.size//2) or (self.gas > (2*(self.size-1)-1)):\n",
    "            self.gas = random.randint(self.size//2, 2*(self.size-1))\n",
    "        self.agent_one = self.DriverAgent(1,self.gas,0) # 3 integer value, when carrying package\n",
    "        self.agent_two = self.DriverAgent(2,int(self.gas*3),0) # 3 integer value, when carrying package\n",
    "        self.spec = EnvSpec(\"SimpleGridRoadEnv-v0\")\n",
    "\n",
    "    def reset(self):\n",
    "        # game like formulation, each player agent moves one step at a time\n",
    "        self.agent_one = self.DriverAgent(1,self.gas,0) # instantiating agent 1 again\n",
    "        self.agent_two = self.DriverAgent(2,int(self.gas*3),0) # instantiating agent 2 again\n",
    "        self.current_player = self.agent_one\n",
    "        # 'P' means the game is playable, 'W' means delivered, 'L' means no delivery\n",
    "        self.state = 'P'\n",
    "        self.current_step = 0\n",
    "        self.max_step = int(self.gas*5) # agent can choose not move as an alternate choice\n",
    "        # selecting a world at random to function with\n",
    "        # even the initial world configuration should be different\n",
    "        # prob = random.uniform(0, 1)\n",
    "        # if prob > 0.25 and prob <= 0.25:\n",
    "        #     self.world_start = self.world_two\n",
    "        # elif prob > 0.5 and prob <= 0.75:\n",
    "        #     self.world_start = self.world_three\n",
    "        # elif prob > 0.75 and prob <= 1:\n",
    "        #     self.world_start = self.world_four\n",
    "        # elif prob < 0.25:\n",
    "        #     self.world_start = self.world_one\n",
    "        self.world_start = self.world_one    \n",
    "        self.world = np.copy(self.world_start) # the self.world can be different from intial world\n",
    "        # no exploration_prize and bonus_reward as per my design\n",
    "        return self._next_observation()\n",
    "    \n",
    "    def _next_observation(self):\n",
    "        obs = self.world\n",
    "        data_to_add = [0] * np.size(self.world, 1)\n",
    "        data_to_add[0] = self.current_player.name # adding current player's label in the observation\n",
    "        obs = np.append(obs, [data_to_add], axis=0)\n",
    "        # observation Sample provided below for reference:\n",
    "        # last row, represents 'data_to_add' vector\n",
    "        # a 3x3 observation grid example for reference\n",
    "        # array([[1, 0, 0],\n",
    "        #         [3, 0, 2],\n",
    "        #         [0, 0, 4],\n",
    "        #         [1, 0, 0]])\n",
    "        return obs\n",
    "\n",
    "    def _take_action(self, action):\n",
    "        # agent's name is matched to the array entries for index identification\n",
    "        # 'current_player.name' should be updated alongside the array values\n",
    "        current_pos = np.where(self.world == self.current_player.name)\n",
    "        # the current agent must have gas in it\n",
    "        if self.current_player.gas > 0:\n",
    "            if action == 0:\n",
    "                next_pos = (current_pos[0] - 1, current_pos[1]) # agent moving upwards\n",
    "\n",
    "                if next_pos[0] >= 0 and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] >= 0 and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two agents can't be at the same place\n",
    "\n",
    "                elif next_pos[0] >= 0 and int(self.world[next_pos] == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] >= 0 and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "\n",
    "\n",
    "            elif action == 1:\n",
    "                next_pos = (current_pos[0], current_pos[1] + 1)\n",
    "                limit = np.size(self.world, 1)\n",
    "\n",
    "                if next_pos[1] < limit and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] < limit and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two Agents can't be at the same place\n",
    "\n",
    "                elif next_pos[1] < limit and (int(self.world[next_pos]) == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent.\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1.\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] < limit and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "\n",
    "\n",
    "            elif action == 2:\n",
    "                next_pos = (current_pos[0] + 1, current_pos[1])\n",
    "                limit = np.size(self.world, 0)\n",
    "\n",
    "                if next_pos[0] < limit and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # teducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] < limit and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two Agents can't be at the same place\n",
    "\n",
    "                elif next_pos[0] < limit and (int(self.world[next_pos]) == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] < limit and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "\n",
    "            elif action == 3:\n",
    "                next_pos = (current_pos[0], current_pos[1] - 1)\n",
    "\n",
    "                if next_pos[1] >= 0 and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] >= 0 and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two Agents can't be at the same place\n",
    "\n",
    "                elif next_pos[1] >= 0 and (int(self.world[next_pos]) == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] >= 0 and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "        else:\n",
    "            # player 1's gas is supposed to go empty first\n",
    "            # therefore, upon having empty gas tank player should be allowed to\n",
    "            # drop the package in the environment and disappear from the location\n",
    "            if self.current_player.package == 3:\n",
    "                self.world[current_pos] = self.current_player.package\n",
    "                # agent dissappears from the grid after this drop\n",
    "            else:\n",
    "                self.world[current_pos] = 0 # if gas is finished, agent should dissappear\n",
    "        # if gas is empty for both agents, the episode should stop at that instant\n",
    "        if self.agent_one.gas == 0 and self.agent_two.gas == 0:\n",
    "            self.state = 'L'\n",
    "\n",
    "\n",
    "    def step(self, action):\n",
    "        self._take_action(action)\n",
    "        self.current_step += 1\n",
    "        # uncomment the below statement out, while debugging\n",
    "        # print(self.world) \n",
    "\n",
    "        if self.state == \"W\":\n",
    "            reward = 5.75\n",
    "            done = True\n",
    "        elif self.state == 'L':\n",
    "            reward = 0\n",
    "            done = True\n",
    "        elif self.state == 'P':\n",
    "            reward = 0 # sparse reward encoding, only rewarded when episode ends\n",
    "            done = False\n",
    "\n",
    "        if self.current_step >= self.max_step:\n",
    "            print(f'New episode number {self.current_episode + 1}')\n",
    "            done = True\n",
    "\n",
    "        # agents object used to identify agent properties\n",
    "        if self.current_player.name == 1 and self.current_step > self.gas*2:\n",
    "            self.current_player = self.agent_two\n",
    "        elif self.current_player.name == 2:\n",
    "            self.current_player = self.agent_one\n",
    "\n",
    "        if done:\n",
    "            self.render_episode(self.state)\n",
    "            self.current_episode += 1\n",
    "\n",
    "        obs = self._next_observation()\n",
    "\n",
    "        return obs, reward, done, {'state': self.state}, self.current_player\n",
    "\n",
    "    def render_episode(self, win_or_lose):\n",
    "        # storing the rendered episodes in a file\n",
    "        self.success_episode.append(\n",
    "            'Success' if win_or_lose == 'W' else 'Failure')\n",
    "        file = open('render.txt', 'a')\n",
    "        file.write('----------------------------\\n')\n",
    "        file.write(f'Episode number {self.current_episode}\\n')\n",
    "        file.write(\n",
    "            f'{self.success_episode[-1]} in {self.current_step} steps\\n')\n",
    "        file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "UXpmVefCHQXN"
   },
   "outputs": [],
   "source": [
    "# the NxN road network environment that rotates in its simple\n",
    "# setting implementation.\n",
    "# implementation of the Simple NxN road network for MultiAgent RL\n",
    "class GridRoadEnv(gym.Env):\n",
    "    # defining the Driving Agent with name and gas values plus package flag\n",
    "    class DriverAgent():\n",
    "        def __init__(self, name, gas, package):\n",
    "            self.name = name\n",
    "            self.gas = gas\n",
    "            self.package = package\n",
    "\n",
    "    def __init__(self, size=5, gas=7):\n",
    "        # size: grid size, gas: values restricted between (n_min: trunc(n/2), n_max: 2*(n-1)-1)\n",
    "        # converting even size to nearest odd number\n",
    "        self.size = size\n",
    "        if size % 2 == 0:\n",
    "            self.size = self.size+1\n",
    "        # defining different possible world configurations\n",
    "        self.world_one = np.zeros((self.size,self.size), dtype=int)\n",
    "        # setting up env values for the numpy array\n",
    "        self.world_one[0][0]=1\n",
    "        self.world_one[self.size-1][self.size-1]=4\n",
    "        self.world_one[self.size//2][self.size-1]=2\n",
    "        self.world_one[self.size//2][0]=3\n",
    "        self.world = self.world_one\n",
    "\n",
    "        self.world_two = np.rot90(self.world_one)\n",
    "        self.world_three = np.rot90(self.world_two)\n",
    "        self.world_four = np.rot90(self.world_three)\n",
    "        # even the initial world configuration is defined to be different upon\n",
    "        # environment instantiation\n",
    "        prob = random.uniform(0, 1)\n",
    "        # default value assignment below\n",
    "        if prob > 0.25 and prob <= 0.25:\n",
    "            self.world = self.world_two\n",
    "        elif prob > 0.5 and prob <= 0.75:\n",
    "            self.world = self.world_three\n",
    "        elif prob > 0.75 and prob <= 1:\n",
    "            self.world = self.world_four\n",
    "        self.world_start = self.world # this 'world_start', if reset() is called, never gets used\n",
    "        # adding five actions for the environment\n",
    "        # 0: up, 1: right, 2: down, 3: left\n",
    "        # when agent reaches at package location it automatically picks up the package\n",
    "        self.action_space = spaces.Discrete(4)\n",
    "        shape_0 = np.size(self.world_start, 0)\n",
    "        shape_1 = np.size(self.world_start, 1)\n",
    "        self.observation_space = spaces.Box(low=0,\n",
    "                                            high=4,\n",
    "                                            shape=(shape_0 + 1, shape_1),\n",
    "                                            dtype=np.int16)\n",
    "        self.reward_range = (-10, 10)\n",
    "        self.current_episode = 0\n",
    "        self.success_episode = []\n",
    "        # defining the driver agents in the environment\n",
    "        self.gas = gas\n",
    "        # truncating the gas to the desirable range for experimentation\n",
    "        if (self.gas < self.size//2) or (self.gas > (2*(self.size-1)-1)):\n",
    "            self.gas = random.randint(self.size//2, 2*(self.size-1))\n",
    "        self.agent_one = self.DriverAgent(1,self.gas,0) # 3 integer value, when carrying package\n",
    "        self.agent_two = self.DriverAgent(2,int(self.gas*3),0) # 3 integer value, when carrying package\n",
    "        self.spec = EnvSpec(\"GridRoadEnv-v0\")\n",
    "\n",
    "    def reset(self):\n",
    "        # game like formulation, each player agent moves one step at a time\n",
    "        self.agent_one = self.DriverAgent(1,self.gas,0) # instantiating agent 1 again\n",
    "        self.agent_two = self.DriverAgent(2,int(self.gas*3),0) # instantiating agent 2 again\n",
    "        self.current_player = self.agent_one\n",
    "        # 'P' means the game is playable, 'W' means delivered, 'L' means no delivery\n",
    "        self.state = 'P'\n",
    "        self.current_step = 0\n",
    "        self.max_step = int(self.gas*5) # agent can choose not move as an alternate choice\n",
    "        # selecting a world at random to function with\n",
    "        # even the initial world configuration should be different\n",
    "        prob = random.uniform(0, 1)\n",
    "        if prob > 0.25 and prob <= 0.25:\n",
    "            self.world_start = self.world_two\n",
    "        elif prob > 0.5 and prob <= 0.75:\n",
    "            self.world_start = self.world_three\n",
    "        elif prob > 0.75 and prob <= 1:\n",
    "            self.world_start = self.world_four\n",
    "        elif prob < 0.25:\n",
    "            self.world_start = self.world_one\n",
    "        # self.world_start = self.world_one    \n",
    "        self.world = np.copy(self.world_start) # the self.world can be different from intial world\n",
    "        # no exploration_prize and bonus_reward as per my design\n",
    "        return self._next_observation()\n",
    "    \n",
    "    def _next_observation(self):\n",
    "        obs = self.world\n",
    "        data_to_add = [0] * np.size(self.world, 1)\n",
    "        data_to_add[0] = self.current_player.name # adding current player's label in the observation\n",
    "        obs = np.append(obs, [data_to_add], axis=0)\n",
    "        # observation Sample provided below for reference:\n",
    "        # last row, represents 'data_to_add' vector\n",
    "        # a 3x3 observation grid example for reference\n",
    "        # array([[1, 0, 0],\n",
    "        #         [3, 0, 2],\n",
    "        #         [0, 0, 4],\n",
    "        #         [1, 0, 0]])\n",
    "        return obs\n",
    "\n",
    "    def _take_action(self, action):\n",
    "        # agent's name is matched to the array entries for index identification\n",
    "        # 'current_player.name' should be updated alongside the array values\n",
    "        current_pos = np.where(self.world == self.current_player.name)\n",
    "        # the current agent must have gas in it\n",
    "        if self.current_player.gas > 0:\n",
    "            if action == 0:\n",
    "                next_pos = (current_pos[0] - 1, current_pos[1]) # agent moving upwards\n",
    "\n",
    "                if next_pos[0] >= 0 and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] >= 0 and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two Agents can't be at the same place\n",
    "\n",
    "                elif next_pos[0] >= 0 and int(self.world[next_pos] == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] >= 0 and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "\n",
    "\n",
    "            elif action == 1:\n",
    "                next_pos = (current_pos[0], current_pos[1] + 1)\n",
    "                limit = np.size(self.world, 1)\n",
    "\n",
    "                if next_pos[1] < limit and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] < limit and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two Agents can't be at the same place\n",
    "\n",
    "                elif next_pos[1] < limit and (int(self.world[next_pos]) == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] < limit and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "\n",
    "\n",
    "            elif action == 2:\n",
    "                next_pos = (current_pos[0] + 1, current_pos[1])\n",
    "                limit = np.size(self.world, 0)\n",
    "\n",
    "                if next_pos[0] < limit and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] < limit and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two Agents can't be at the same place\n",
    "\n",
    "                elif next_pos[0] < limit and (int(self.world[next_pos]) == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[0] < limit and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "\n",
    "            elif action == 3:\n",
    "                next_pos = (current_pos[0], current_pos[1] - 1)\n",
    "\n",
    "                if next_pos[1] >= 0 and int(self.world[next_pos]) == 0:\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] >= 0 and int(self.world[next_pos]) in (1, 2):\n",
    "                    pass # two Agents can't be at the same place\n",
    "\n",
    "                elif next_pos[1] >= 0 and (int(self.world[next_pos]) == 3):\n",
    "                    self.world[next_pos] = self.current_player.name\n",
    "                    self.current_player.package = 3 # package is also hidden now from other agent\n",
    "                    self.world[current_pos] = 0\n",
    "                    # reducing the agent's gas by 1\n",
    "                    self.current_player.gas = self.current_player.gas - 1\n",
    "\n",
    "                elif next_pos[1] >= 0 and int(self.world[next_pos] == 4):\n",
    "                    # player should only be allowed this transition to this position\n",
    "                    # when it is having the package with it\n",
    "                    if self.current_player.package == 3:\n",
    "                        self.world[next_pos] = self.current_player.name\n",
    "                        self.world[current_pos] = 0\n",
    "                        self.state = 'W' # and the episode, should end at that moment\n",
    "                        # reducing the agent's gas by 1\n",
    "                        self.current_player.gas = self.current_player.gas - 1\n",
    "                    else:\n",
    "                        pass\n",
    "\n",
    "        else:\n",
    "            # player 1's gas is supposed to go empty first\n",
    "            # therefore, upon having empty gas tank player should be allowed to\n",
    "            # drop the package in the environment and disappear from the location\n",
    "            if self.current_player.package == 3:\n",
    "                self.world[current_pos] = self.current_player.package\n",
    "                # agent dissappears from the grid after this drop\n",
    "            else:\n",
    "                self.world[current_pos] = 0 # if gas is finished, agent should dissappear\n",
    "        # if gas is empty for both agents, the episode should stop at that instant\n",
    "        if self.agent_one.gas == 0 and self.agent_two.gas == 0:\n",
    "            self.state = 'L'\n",
    "\n",
    "\n",
    "    def step(self, action):\n",
    "        self._take_action(action)\n",
    "        self.current_step += 1\n",
    "        # uncomment the below statement out, while debugging\n",
    "        # print(self.world) \n",
    "\n",
    "        if self.state == \"W\":\n",
    "            reward = 5.75\n",
    "            done = True\n",
    "        elif self.state == 'L':\n",
    "            reward = 0\n",
    "            done = True\n",
    "        elif self.state == 'P':\n",
    "            reward = 0 # sparse reward encoding, only rewarded when episode ends\n",
    "            done = False\n",
    "\n",
    "        if self.current_step >= self.max_step:\n",
    "            print(f'New episode number {self.current_episode + 1}')\n",
    "            done = True\n",
    "\n",
    "        # agents object used to identify agent properties\n",
    "        if self.current_player.name == 1 and self.current_step > self.gas*2:\n",
    "            self.current_player = self.agent_two\n",
    "        elif self.current_player.name == 2:\n",
    "            self.current_player = self.agent_one\n",
    "\n",
    "        if done:\n",
    "            self.render_episode(self.state)\n",
    "            self.current_episode += 1\n",
    "\n",
    "        obs = self._next_observation()\n",
    "\n",
    "        return obs, reward, done, {'state': self.state}, self.current_player\n",
    "\n",
    "    def render_episode(self, win_or_lose):\n",
    "        # storing the rendered episodes in a file\n",
    "        self.success_episode.append(\n",
    "            'Success' if win_or_lose == 'W' else 'Failure')\n",
    "        file = open('render.txt', 'a')\n",
    "        file.write('----------------------------\\n')\n",
    "        file.write(f'Episode number {self.current_episode}\\n')\n",
    "        file.write(\n",
    "            f'{self.success_episode[-1]} in {self.current_step} steps\\n')\n",
    "        file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Nod7A67cCyEt"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='governance-wrapper-layer-implementation'></a>\n",
    "#### 2. Governance Wrapper Layer Implementation\n",
    "\n",
    "The `SimpleGovernanceKernelWrapper` class inherits a specific environment, and adds reward distribution based wrapper functionalities to give dense reward signals during agent transitions.\n",
    "Additionally, this layer also contains functionality to add in the directional governance component as well for better agent guidance and comparison with MORS baseline approach.\n",
    "Second, the `GreedyGovernanceKernelWrapper` class functions in a similar manner except the reward distribution decreases as the agent traverses the environment.\n",
    "The inline comments elaborates the functionalities of the implemented governance layer in more detail for more granular understanding of different components in the governance layer.\n",
    "\n",
    "**Note:** The current design limits inheriting only a specific kind of class environment for a defined governance wrapper layer.\n",
    "Therefore, for every experiment we have to replace and update the inherited parent environment class multiple times to carry out different experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "VSDoXxRXa197"
   },
   "outputs": [],
   "source": [
    "# basic conceptual framework of the governance wrapper:\n",
    "# * environment prior information available, like initial agent positions and corresponding gas info.,\n",
    "# * environment implementation needs change, agent info. or the whole agent instance needs to be returned as well\n",
    "# * iterative radial reward value decrease by a factor of 5 from initial movement step from 0.25 up until {gas/dim} boundary\n",
    "# * noise addition in the values, based on the grid size for size 3 noise factor zero, but with larger grid size, larger noise, for exploration promotion\n",
    "# * partial task completion reward, 0.25 reward value attained upon package pickup by the agent\n",
    "# * prior reward shaped grid based on basic environment information whill be matched with shared observation state for new reward calculation\n",
    "# * additionally, create a simple reward shaping environment that only assigns 0.25 reward value upon package pickup by the agent\n",
    "\n",
    "# environments to inherit from:\n",
    "# SizeThreeGridRoadEnv, SizeThreeSimpleGridRoadEnv, SimpleGridRoadEnv, GridRoadEnv\n",
    "class SimpleGovernanceKernelWrapper(SimpleGridRoadEnv): \n",
    "\n",
    "    def governance_reward(self, observation, kernel, agent_name, prev_obs):\n",
    "        agent_ind_x, agent_ind_y = np.where(observation == agent_name)\n",
    "        prev_agent_ind_x, prev_agent_ind_y = np.where(prev_obs == agent_name)\n",
    "        prev_obs = observation # saving the previous observation state\n",
    "        # if agent is not present in the observation grid, return 0 reward\n",
    "        if agent_ind_x[0] >= kernel.shape[0] or agent_ind_y[0] >= kernel.shape[1]: \n",
    "            return 0, prev_obs\n",
    "        # if agent is still at present at the previous grid position, return 0 reward\n",
    "        if agent_ind_x[0] == prev_agent_ind_x[0] and agent_ind_y[0] == prev_agent_ind_y[0]: \n",
    "            return 0, prev_obs\n",
    "        return round(kernel[agent_ind_x[0]][agent_ind_y[0]],2), prev_obs\n",
    "\n",
    "    def gradient_kernel(self, world_map):\n",
    "        grad_kernel = np.zeros((world_map.shape[0], world_map.shape[1]))\n",
    "        grad_unit = 0.025\n",
    "        for i in range(0,world_map.shape[0]):\n",
    "            for j in range(0,world_map.shape[1]):\n",
    "                if i>=j:\n",
    "                    grad_kernel[i][j] = grad_unit*(i+j)\n",
    "                else:\n",
    "                    grad_kernel[i][j] = grad_unit*(i-j)\n",
    "        return grad_kernel\n",
    "\n",
    "    def circular_kernel(self, world_map, agent_name):\n",
    "        agent_ind_x, agent_ind_y = np.where(world_map == agent_name)\n",
    "        grad_kernel = np.zeros((world_map.shape[0], world_map.shape[1]))\n",
    "        grad_unit = 0.075\n",
    "        # print(agent_ind_x, agent_ind_y)\n",
    "        for i in range(0,world_map.shape[0]):\n",
    "            for j in range(0,world_map.shape[1]):\n",
    "                if i != agent_ind_x[0] and j != agent_ind_y[0]:\n",
    "                    grad_kernel[i][j] = 1/2*(grad_unit/(i-agent_ind_x[0])**2  +  grad_unit/(j-agent_ind_y[0])**2)\n",
    "                elif i == agent_ind_x[0] and j != agent_ind_y[0]:\n",
    "                    grad_kernel[i][j] = grad_unit/(j-agent_ind_y[0])**2\n",
    "                elif j == agent_ind_y[0] and i != agent_ind_x[0]:\n",
    "                    grad_kernel[i][j] = grad_unit/(i-agent_ind_x[0])**2\n",
    "                elif i == agent_ind_x[0] and j == agent_ind_y[0]:\n",
    "                    grad_kernel[i][j] = 0\n",
    "        return grad_kernel\n",
    "    \n",
    "    # superposition of governance kernels for generating the effective kernel output for both the agents\n",
    "    # print(np.add(gradient_kernel(world_map), circular_kernel(world_map, 1)))\n",
    "    # print(np.add(gradient_kernel(world_map), circular_kernel(world_map, 2)))\n",
    "\n",
    "    def __init__(self, radial=False):\n",
    "        super().__init__()\n",
    "        self.prev_obs_agent_one = self.world_start\n",
    "        self.prev_obs_agent_two = self.world_start\n",
    "        self.radial_flag = radial\n",
    "        if self.radial_flag == False:\n",
    "            self.gov_kern_agent_one = np.add(self.gradient_kernel(self.world_start), self.circular_kernel(self.world_start, 1))\n",
    "            self.gov_kern_agent_two = np.add(self.gradient_kernel(self.world_start), self.circular_kernel(self.world_start, 2))\n",
    "        else:\n",
    "            self.gov_kern_agent_one = self.circular_kernel(self.world_start, 1)\n",
    "            self.gov_kern_agent_two = self.circular_kernel(self.world_start, 2)\n",
    "        self.preward_flag_agent_one = True\n",
    "        self.preward_flag_agent_two = True\n",
    "\n",
    "    def reset(self):\n",
    "        self.prev_obs_agent_one = self.world_start\n",
    "        self.prev_obs_agent_two = self.world_start\n",
    "        if self.radial_flag == False:\n",
    "            self.gov_kern_agent_one = np.add(self.gradient_kernel(self.world_start), self.circular_kernel(self.world_start, 1))\n",
    "            self.gov_kern_agent_two = np.add(self.gradient_kernel(self.world_start), self.circular_kernel(self.world_start, 2))\n",
    "        else:\n",
    "            self.gov_kern_agent_one = self.circular_kernel(self.world_start, 1)\n",
    "            self.gov_kern_agent_two = self.circular_kernel(self.world_start, 2)\n",
    "        self.preward_flag_agent_one = True\n",
    "        self.preward_flag_agent_two = True\n",
    "        return super().reset()\n",
    "\n",
    "    def step(self, action):\n",
    "        next_state, reward, done, info, agent = super().step(action)\n",
    "        reward_new = 0\n",
    "        if agent.name == 1:\n",
    "            # print(next_state, self.prev_obs_agent_one)\n",
    "            reward_new, self.prev_obs_agent_one = self.governance_reward(next_state, self.gov_kern_agent_one, agent.name, self.prev_obs_agent_one)\n",
    "            # print(reward_new, self.prev_obs_agent_one, agent.name)\n",
    "        elif agent.name == 2:\n",
    "            # print(next_state, self.prev_obs_agent_two)\n",
    "            reward_new, self.prev_obs_agent_two = self.governance_reward(next_state, self.gov_kern_agent_two, agent.name, self.prev_obs_agent_two)\n",
    "            # print(reward_new, self.prev_obs_agent_two, agent.name)\n",
    "        # print('\\n')\n",
    "\n",
    "        if agent.package == 3 and self.preward_flag_agent_one == True and agent.name == 1:\n",
    "            reward_new = reward_new + 0.05\n",
    "            self.preward_flag_agent_one = False\n",
    "            # print(reward_new, agent.name)\n",
    "        elif agent.package == 3 and self.preward_flag_agent_two == True and agent.name == 2:\n",
    "            reward_new = reward_new + 0.05\n",
    "            self.preward_flag_agent_two = False\n",
    "            # print(reward_new, agent.name)\n",
    "\n",
    "        reward_new  = round(reward_new, 2) + reward\n",
    "        return next_state, reward_new, done, info # , agent\n",
    "        #, agent; will give too values to unpack error for the default stablebaselines model implementation\n",
    "\n",
    "# current checks, the reward shaping with kernel is working correctly,\n",
    "# needs to add reward for successfully picking up the package as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "NyC2BU_OYyTW"
   },
   "outputs": [],
   "source": [
    "# greedy governance implementation process:\n",
    "# * 'governance_reward' function changes 'self.gov_kern_agent_one' & 'self.gov_kern_agent_one'\n",
    "# * with probability epsilon, the select reward from the reward grid & reduce by given factor\n",
    "# implementation process for random rotation models:\n",
    "# kernel initialization should happen based on the newly initialized environment topology\n",
    "# two governance alternative designs: baseline and greedy governance for experimentation\n",
    "\n",
    "# basic conceptual framework of the governance wrapper:\n",
    "# * environment prior information available, like initial agent positions and corresponding gas info.,\n",
    "# * environment implementation needs change, agent info. or the whole agent instance needs to be returned as well\n",
    "# * iterative radial reward value decrease by a factor of 5 from initial movement step from 0.25 up until {gas/dim} boundary\n",
    "# * noise addition in the values, based on the grid size for size 3 noise factor zero, but with larger grid size, larger noise, for exploration promotion\n",
    "# * partial task completion reward, 0.25 reward value attained upon package pickup by the agent\n",
    "# * prior reward shaped grid based on basic environment information whill be matched with shared observation state for new reward calculation\n",
    "# * additionally, create a simple reward shaping environment that only assigns 0.25 reward value upon package pickup by the agent\n",
    "\n",
    "# environments to inherit from:\n",
    "# SizeThreeGridRoadEnv, SizeThreeSimpleGridRoadEnv, SimpleGridRoadEnv, GridRoadEnv\n",
    "class GreedyGovernanceKernelWrapper(SimpleGridRoadEnv):\n",
    "\n",
    "    def governance_reward(self, observation, kernel, agent_name, prev_obs):\n",
    "        agent_ind_x, agent_ind_y = np.where(observation == agent_name)\n",
    "        prev_agent_ind_x, prev_agent_ind_y = np.where(prev_obs == agent_name)\n",
    "        prev_obs = observation # saving the previous observation state\n",
    "        # if agent is not present in the observation grid, return 0 reward\n",
    "        if agent_ind_x[0] >= kernel.shape[0] or agent_ind_y[0] >= kernel.shape[1]: \n",
    "            return 0, prev_obs\n",
    "        # if agent is still at present at the previous grid position, return 0 reward\n",
    "        if agent_ind_x[0] == prev_agent_ind_x[0] and agent_ind_y[0] == prev_agent_ind_y[0]: \n",
    "            return 0, prev_obs\n",
    "\n",
    "        kernel_reward_val = kernel[agent_ind_x[0]][agent_ind_y[0]] \n",
    "\n",
    "        if agent_name == 1:\n",
    "            self.gov_kern_agent_one[agent_ind_x[0]][agent_ind_y[0]] = kernel[agent_ind_x[0]][agent_ind_y[0]] / 2\n",
    "        if agent_name == 2:\n",
    "            self.gov_kern_agent_two[agent_ind_x[0]][agent_ind_y[0]] = kernel[agent_ind_x[0]][agent_ind_y[0]] / 2\n",
    "\n",
    "        \n",
    "        return round(kernel_reward_val, 2), prev_obs\n",
    "\n",
    "    def gradient_kernel(self, world_map):\n",
    "        grad_kernel = np.zeros((world_map.shape[0], world_map.shape[1]))\n",
    "        grad_unit = 0.01\n",
    "        for i in range(0,world_map.shape[0]):\n",
    "            for j in range(0,world_map.shape[1]):\n",
    "                if i>=j:\n",
    "                    grad_kernel[i][j] = grad_unit*(i+j)\n",
    "                else:\n",
    "                    grad_kernel[i][j] = grad_unit*(i-j)\n",
    "        return grad_kernel\n",
    "\n",
    "    def circular_kernel(self, world_map, agent_name):\n",
    "        agent_ind_x, agent_ind_y = np.where(world_map == agent_name)\n",
    "        grad_kernel = np.zeros((world_map.shape[0], world_map.shape[1]))\n",
    "        grad_unit = 0.05\n",
    "        # print(agent_ind_x, agent_ind_y)\n",
    "        for i in range(0,world_map.shape[0]):\n",
    "            for j in range(0,world_map.shape[1]):\n",
    "                if i != agent_ind_x[0] and j != agent_ind_y[0]:\n",
    "                    grad_kernel[i][j] = 1/2*(grad_unit/(i-agent_ind_x[0])**2  +  grad_unit/(j-agent_ind_y[0])**2)\n",
    "                elif i == agent_ind_x[0] and j != agent_ind_y[0]:\n",
    "                    grad_kernel[i][j] = grad_unit/(j-agent_ind_y[0])**2\n",
    "                elif j == agent_ind_y[0] and i != agent_ind_x[0]:\n",
    "                    grad_kernel[i][j] = grad_unit/(i-agent_ind_x[0])**2\n",
    "                elif i == agent_ind_x[0] and j == agent_ind_y[0]:\n",
    "                    grad_kernel[i][j] = 0\n",
    "        return grad_kernel\n",
    "    \n",
    "    # superposition of governance kernels for generating the effective kernel output for both the agents\n",
    "    # print(np.add(gradient_kernel(world_map), circular_kernel(world_map, 1)))\n",
    "    # print(np.add(gradient_kernel(world_map), circular_kernel(world_map, 2)))\n",
    "\n",
    "    def __init__(self, radial=False):\n",
    "        super().__init__()\n",
    "        self.prev_obs_agent_one = self.world_start\n",
    "        self.prev_obs_agent_two = self.world_start\n",
    "        self.radial_flag = radial\n",
    "        if self.radial_flag == False:\n",
    "            self.gov_kern_agent_one = np.add(self.gradient_kernel(self.world_start), self.circular_kernel(self.world_start, 1))\n",
    "            self.gov_kern_agent_two = np.add(self.gradient_kernel(self.world_start), self.circular_kernel(self.world_start, 2))\n",
    "        else:\n",
    "            self.gov_kern_agent_one = self.circular_kernel(self.world_start, 1)\n",
    "            self.gov_kern_agent_two = self.circular_kernel(self.world_start, 2)\n",
    "        self.preward_flag_agent_one = True\n",
    "        self.preward_flag_agent_two = True\n",
    "\n",
    "    def reset(self):\n",
    "        self.prev_obs_agent_one = self.world_start\n",
    "        self.prev_obs_agent_two = self.world_start\n",
    "        if self.radial_flag == False:\n",
    "            self.gov_kern_agent_one = np.add(self.gradient_kernel(self.world_start), self.circular_kernel(self.world_start, 1))\n",
    "            self.gov_kern_agent_two = np.add(self.gradient_kernel(self.world_start), self.circular_kernel(self.world_start, 2))\n",
    "        else:\n",
    "            self.gov_kern_agent_one = self.circular_kernel(self.world_start, 1)\n",
    "            self.gov_kern_agent_two = self.circular_kernel(self.world_start, 2)\n",
    "        self.preward_flag_agent_one = True\n",
    "        self.preward_flag_agent_two = True\n",
    "        return super().reset()\n",
    "\n",
    "    def step(self, action):\n",
    "        next_state, reward, done, info, agent = super().step(action)\n",
    "        reward_new = 0\n",
    "        if agent.name == 1:\n",
    "            # print(next_state, self.prev_obs_agent_one)\n",
    "            reward_new, self.prev_obs_agent_one = self.governance_reward(next_state, self.gov_kern_agent_one, agent.name, self.prev_obs_agent_one)\n",
    "            # print(reward_new, self.prev_obs_agent_one, agent.name)\n",
    "        elif agent.name == 2:\n",
    "            # print(next_state, self.prev_obs_agent_two)\n",
    "            reward_new, self.prev_obs_agent_two = self.governance_reward(next_state, self.gov_kern_agent_two, agent.name, self.prev_obs_agent_two)\n",
    "            # print(reward_new, self.prev_obs_agent_two, agent.name)\n",
    "        # print('\\n')\n",
    "\n",
    "        if agent.package == 3 and self.preward_flag_agent_one == True and agent.name == 1:\n",
    "            reward_new = reward_new + 0.05\n",
    "            self.preward_flag_agent_one = False\n",
    "            # print(reward_new, agent.name)\n",
    "        elif agent.package == 3 and self.preward_flag_agent_two == True and agent.name == 2:\n",
    "            reward_new = reward_new + 0.05\n",
    "            self.preward_flag_agent_two = False\n",
    "            # print(reward_new, agent.name)\n",
    "\n",
    "        reward_new  = round(reward_new, 2) + reward\n",
    "        return next_state, reward_new, done, info # , agent\n",
    "        #, agent; will give too values to unpack error for the default stablebaselines model implementation\n",
    "\n",
    "# current checks, the reward shaping with kernel is working correctly,\n",
    "# needs to add reward for successfully picking up the package as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='rl-algorithm-model-training'></a>\n",
    "#### 3. RL Algorithm Model Training\n",
    "\n",
    "The `stable_baselines3` package implementation code for all the trained models as part of this experiment is presented in this section.\n",
    "The naming convention of the trained model follows the specified order `{rl-algo}_model_{grid-size}_{orientation}`, and is uniquely defined for each of the experiment model variants.\n",
    "Additionally, even though the `SimpleGovernanceKernelWrapper` and `GreedyGovernanceKernelWrapper` are the same for the models with incorporated governance layers, we manually change the inherited environment class before in the above governance layer section.\n",
    "\n",
    "This section also defines basic plotter function to quickly observe the convergence plot, and get an idea about the initial model performance.\n",
    "Also, we manually output the entire logging file generated by `Monitor` wrapper, and save it for further creating our experiment summaries in a more presentable manner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "bpbJzTgt5LcI"
   },
   "outputs": [],
   "source": [
    "# stable_baseline3 library related import statements\n",
    "# from stable_baselines3.common.vec_env import DummyVecEnv # it's usage produces 'spec' related error\n",
    "# from stable_baselines3.common.vec_env import SubprocVecEnv # it's usage produces 'spec' related error\n",
    "from stable_baselines3 import A2C\n",
    "from stable_baselines3 import PPO\n",
    "# result plotter package function\n",
    "from stable_baselines3.common import results_plotter\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# important note: implementation limitation, currently both governance variants\n",
    "# inherit specific environments during initialization & these wrappers are hard binded\n",
    "# to the code. Hence, need to be updated for manual experimentation with different environments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "ZDcvSMZ570LX"
   },
   "outputs": [],
   "source": [
    "# ploting function utilities for smoothed moving average reward values\n",
    "def moving_average(values, window):\n",
    "    \"\"\"\n",
    "    Smooth values by doing a moving average\n",
    "    :param values: (numpy array)\n",
    "    :param window: (int)\n",
    "    :return: (numpy array)\n",
    "    \"\"\"\n",
    "    weights = np.repeat(1.0, window) / window\n",
    "    return np.convolve(values, weights, 'valid')\n",
    "\n",
    "def plot_results(log_folder, title='Learning Curve'):\n",
    "    \"\"\"\n",
    "    plot the results\n",
    "\n",
    "    :param log_folder: (str) the save location of the results to plot\n",
    "    :param title: (str) the title of the task to plot\n",
    "    \"\"\"\n",
    "    x, y = ts2xy(load_results(log_folder), 'timesteps')\n",
    "    y = moving_average(y, window=100)\n",
    "    # Truncate x\n",
    "    x = x[len(x) - len(y):]\n",
    "\n",
    "    fig = plt.figure(title)\n",
    "    plt.plot(x, y)\n",
    "    plt.xlabel('Number of Timesteps')\n",
    "    plt.ylabel('Rewards')\n",
    "    plt.title(title + \" \")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LJjDiyrTkN8O"
   },
   "outputs": [],
   "source": [
    "# creating the environment for code compilation testing only\n",
    "# env_simple = RewardShapedGridRoadEnv()\n",
    "# Logs will be saved in log_dir/monitor.csv\n",
    "# env_simple = Monitor(env_simple, log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dx5z0giWkOC8"
   },
   "outputs": [],
   "source": [
    "env_simple = SimpleGovernanceKernelWrapper(radial=True)\n",
    "# logs will be saved in log_dir/monitor.csv\n",
    "env_simple = Monitor(env_simple, log_dir)\n",
    "# training sample environment policy for checking the env functionality\n",
    "callback = SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir)\n",
    "\n",
    "ppo_model_3x3_simple = PPO(\"MlpPolicy\", env_simple, verbose=0)\n",
    "ppo_model_3x3_simple.learn(total_timesteps=75000, callback=callback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "id": "G_kGxZyl8Z2_",
    "outputId": "b43fb834-cee4-46d7-e178-7dc828683bf0"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helper function from the ploting library\n",
    "results_plotter.plot_results([log_dir], 1e5, results_plotter.X_TIMESTEPS, \"PPO 3x3 Road Env Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "AaSBY3oS8hVa",
    "outputId": "a3e0d1e2-b068-4c12-e758-06466fac4b35"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reward function value curve\n",
    "plot_results(log_dir, \"PPO 3x3 Road Env Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZOAF0NEKK2G8"
   },
   "outputs": [],
   "source": [
    "# outputting the logged reward values\n",
    "!cat /tmp/gym/monitor.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qjutkXgF8Wp8"
   },
   "outputs": [],
   "source": [
    "env_simple = GreedyGovernanceKernelWrapper(radial=True)\n",
    "# logs will be saved in log_dir/monitor.csv\n",
    "env_simple = Monitor(env_simple, log_dir)\n",
    "# training sample environment policy for checking the env functionality\n",
    "callback = SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir)\n",
    "\n",
    "ppo_model_3x3_greedy = PPO(\"MlpPolicy\", env_simple, verbose=0)\n",
    "ppo_model_3x3_greedy.learn(total_timesteps=75000, callback=callback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "id": "3pTQgoOn8WsA",
    "outputId": "7bdbba21-79c7-46f4-e45c-30f02a53440d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helper function from the ploting library\n",
    "results_plotter.plot_results([log_dir], 1e5, results_plotter.X_TIMESTEPS, \"PPO 3x3 Road Env Greedy Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "eGQAVbSS8Wth",
    "outputId": "fa5d5559-ba84-419a-cd46-a371d10eb9d7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reward function value curve\n",
    "plot_results(log_dir, \"PPO 3x3 Road Env Greedy Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "G5wbIlsTL88p"
   },
   "outputs": [],
   "source": [
    "# outputting the logged reward values\n",
    "!cat /tmp/gym/monitor.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "a2tMCdn98Wvj"
   },
   "outputs": [],
   "source": [
    "env_random = SimpleGovernanceKernelWrapper(radial=True)\n",
    "# logs will be saved in log_dir/monitor.csv\n",
    "env_random = Monitor(env_random, log_dir)\n",
    "# training sample environment policy for checking the env functionality\n",
    "callback = SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir)\n",
    "\n",
    "ppo_model_3x3_random = PPO(\"MlpPolicy\", env_random, verbose=0)\n",
    "ppo_model_3x3_random.learn(total_timesteps=75000, callback=callback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "id": "u5N_rhihBvnx",
    "outputId": "6d5f5d40-5cab-4207-f14e-41709c3adbe3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helper function from the ploting library\n",
    "results_plotter.plot_results([log_dir], 1e5, results_plotter.X_TIMESTEPS, \"PPO 3x3 Random Road Env Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "R6IORUpBBv3v",
    "outputId": "ad70fbed-76a1-4702-bb32-200d04b47b27"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reward function value curve\n",
    "plot_results(log_dir, \"PPO 3x3 Random Road Env Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "F2TQ4XnO8WxG"
   },
   "outputs": [],
   "source": [
    "# outputting the logged reward values\n",
    "!cat /tmp/gym/monitor.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "C8chkfvb8Wzy"
   },
   "outputs": [],
   "source": [
    "env_random = GreedyGovernanceKernelWrapper(radial=True)\n",
    "# logs will be saved in log_dir/monitor.csv\n",
    "env_random = Monitor(env_random, log_dir)\n",
    "# training sample environment policy for checking the env functionality\n",
    "callback = SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir)\n",
    "\n",
    "ppo_model_3x3_random_greedy = PPO(\"MlpPolicy\", env_random, verbose=0)\n",
    "ppo_model_3x3_random_greedy.learn(total_timesteps=75000, callback=callback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "id": "WJtUoz0_5A-N",
    "outputId": "ccdc93ae-d034-4b38-f262-bf07a69e5a60"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helper function from the ploting library\n",
    "results_plotter.plot_results([log_dir], 1e5, results_plotter.X_TIMESTEPS, \"PPO 3x3 Random Road Env Greedy Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "7plf_2xjhSnm",
    "outputId": "90380809-ba61-4bf5-815f-193daee0e1ea"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WvOwMhhblUdPYTH1TmPpQmLqmMJuqGqhtbCaisGprLRFV1myrZ2RpHsX52VTWhVi+pZZ12+tB4L0fH0NJfjaRiPLlO97qMoZRpfnsMaKY0oJsMgQaQhHqmprJycrgyN2GUFaYS1lhDvuMLmVYcR7NEXeW3tgcYUtNIwClBdkUZGeRl50BAhV1IZrDSjiiNEci/r8SCkfIycygKC+b3OyMNmfuEj3Dbz3LzwgOy4CsjAxyslwpIxSOUNsYpq6pOVqCiCiEfUmjtfTRWsIIhZV1FfUsLa92pZ71VXzUFGZ7bRMRVQpysijJz2bsoAJO328UI0vzKcjJJCszg2xfwinNz2ZIUS652RnkZGaQ7f8yM1y84GKHtu8DlZZxSEKlk11hCcGYGP98bzX/9fB8Dp9SRlFeFh+urkCA/JxMvnHQOM4/dMIuzX/xxmreWb6VLTWN1DW5M/PG5jAbKhsozM2itqmZVVvryMwQBPjVGfsyZpB71e7f3lnFHa98xqaqBpojiT2p2B1IsyjMy2LNtjrKqxvJzXIHojGD8hk7qIBXl2xm/tpKRg/Mp6bRvUH1hL2Hc94h4xlalEt2ZgZrttcxICeLorwsivOzKStM6GVicU0eWhh3+NCivJ2eZyIyMzLJy86kKFAa2hWRiCLSeiBPd5YQTI+pagjR0BRmaHFyf/QAjc1httU2MaIkf4c/O39dJQBrttURCisThwxgU1Ujy8preHr+xl1OCL94dhGzF5WTmSHkZWVQG6heyMoQivOzGTQgh4q6JrbUNPH+qu3RhHDPmytYV1HPt4+YyO7Dixg3uIDV2+pQhbLCXIryssjPySQ/O5PS/BwamsMMLcrt9ID12pLNvLpkM+f8eU6b4QdPGsxBEwdH+1tiMK0yMvpGImhhCcH0mCsf/JA5y7fx6U1fSNoyZi/axBPz1vPswo00hCKUFeawpaaJHx6/O5cfPbnNtM8v3MiCdZUs21yDKqyvqKe6sZnlm2sZUpTLKz88us30l/7tfZ5ZsJFv/HkOfzx7BkV52fztnVVkZwpzV27nhGnDmTq8mBv/8wlfmzWGB95ZxZCiXBZtrKYhFGFESR7NEeW1JZsZPTCfZ757OEV52VTWhyjMzSIcUbIzJXrwrqwLse+Nz3Pf2yuZv66S+lCY5ZtrOWHv4Vx7Yuu73/cf13GdcgldnwkfNHEw1504lfycLHJ9aSI/J7NNMjD9gyUE020iEe3wjGldRT2vLN68Q/NrDrtGyUTr8iMR5YJ73ZNwdx9WxOJN1WypaQLglucWc+FhE8jLzoxOf/Mzi1i+pTbav/+4gYweWMAxewzjkEntD4ZnzBxNeXUjry/dwqKN1QwvzuP6xxZEx6/ZXseZB4zl2YUbeXbhxnafbwiFGZCbyciSPE6dPjJabVGS7/5nxmy74vwsjttzGAvXV/H3OaupD4XJEBdnd8rJyuDiIyZ16zxNerKEYLrFmm11HP2rV/jj2TM4bq/h7cb/+NH50e5EG2yn3fA89aEwC//neAbkdv5VfX/Vdn757CIAfvSF3bnkiElMvO5pAK44ejK3v7yMZxds5MmPNzBvTQV/Ond/lm+ppSgvi+oGV2f+8KWHdLqMz00dxrDiPE667Q2+99A8po9pe2B+Z/k23lm+rc2wJ79zGCf//g0AZn//yB2qaxYR7jp3ZsLTG7Oreu9lFCatfLqhiuaI8s/31sQdHywdbKhs6HJ+VQ0h6kOubn3Jpuoup7/m4Y+Zs8IdjMcNGkBGhnD2gWPZf9xATpw2AoCr/jmPFz/dxJaaRh6auxaAcw8eB8CZB4zpchngGkOP2WMYdY1h/vPR+ujwrAxhSFEuB05w1TejB+Yza8IgJg0p5Ev7jeLMA8b0mYZH03dZCcHssmfmb+DSBz4AiF6hErSpqm0CeGDOaq45YWqn8zzht69Hu8urGzucrqaxmb++vYql5TXRYSdOcyWUn50+LTrsqzNHR5MAwIPvrqasMJcfHj+VHxy3e8IH69ysTO4+z521V9aHyMqQLksvt35tekLzNibVrIRgdtmT8zdEu+NdQfToh+sA+OWX9wHgzlc/485XPyPcyWWT6yrqo92NzR3f4PTWsi38wlcVAZw1K/6Z+J4jitsNu/gId7XQzp65l+Rnd5kMjEkn9m02uywSOLCHYg7ekYhy8zPugL3HiGKmjyll3poKbn5mEYdOKmPa6JJ281NVMjOEL88YxUNz1/L0xxs4Zd+RbaZZs62ODZUN/PbFpQD8/qz9OH6v4WRnxj+4n3fIeA6aNJihRXkMGpCzS+trTF9lCcHssoZQ63X022qb2oxrabAFmDa6hHu/eQCzF5XzvYc+Yvai8nYJobohxPMLNxGOKBPK3M1LGXHKsYf/8uU2/UftPqTTuzhFhKnD25cSjDGtrMrI7LR1FfVcdN97vOwbjE+cNpx3V25r046wYH1lm8+UFuRw2OQyAH7z4hLe+mxLm/F3vvoZ3//XRwBkZwqzJgxia03bJBNrznWf77Y7T43pzywhmJ32uxeX8OKn5QB879jd2Gd0KQAPzlkdnaal9PDgtw6KDgu2M7y/cnubea7e1tp2MKo0n8EDctgaU+p4d0XbSztbruM3xuwaSwhmh6kqP3vqk+hVOytvPokrPz+Fbx8xkZzMDB7/aF20XaHlqY9lhW3r7ffxVUUvfrqpzfA5y7dGu0eU5jO4MIdl5TW8uqT1stU12+rafCZ4s5kxZudZQjA7rLYpzJ9eXwHASf4af3D19E3hCAvWVXH7y8sAovcS5Oe0PWjff8EsAAYX5tIcjkSrmeqbwhw8cTD3nD+TfUeXcNr0UQA85q9UAvjbnFWAu9Y/35KBMd3GGpVNwj5ZX8XS8uroZZr/c8penHfI+DbTTB1exKKN1fzp9eV853OTqfclhNgDd2lBDmWFucxeVM7kHz8DwJf2G0V1YzMThgzgc1OHATBz/CCmDi/i0Q/XcdasscyaMAhVGFiQzTPfPZzEnvdpjEmElRBMwk687XW++495XPngh0D8uvuWu4KrG5p5dsFGXlrk2hgKctqfe9x5zgxGlLS2JzziSwEzxrZ9JMQX/SWnLaWOuqZmDpwwmKK87DYvdTHG7BpLCCYhkTg3ke09qv09BJccOYmfnb43AB+treQ1X/efG+eS0JnjB/EN/+iIoKnDi9r0X370ZA6fUsZrSzazZFM122pDFORYVZEx3c0SgknI5pr2j4+Id1DOycrg7APdQf7OVz8D4MrPT+nwKajDYl6IMm5wQdy7igcWuEbp437zGltqGtlY1fXzkIwxO8YSgklIVX2o3bB4Z/0tLjmy9XHKe44o6nC64SVtE8Jhk8viJo89YpLE+LIBHc7TGLNzktaoLCJjgPuBYYACd6nq72KmOQp4HFjhBz2iqjcmKyaz82rjvDS8MK/jr881J0zlmhOmUlkXoji/4+n2HVMa7T55nxF8ef/RcafbfXjbVy7GK0UYY3ZNMq8yaga+r6ofiEgR8L6IvKCqn8RM97qqnpzEOEw3qPOXhf78S9OYWDaA6WNLyc3quh6/pKDzRt9C/3C4cYMLuP3rMzqc7ujdh3LP+TMZkJPF+sp6jtlj2A5Eb4xJRNISgqpuADb47moR+RQYBcQmBJMGWkoI00aVxG1M3hUPX3oww7t497GIRC9FNcYkR4+0IYjIeGA/YE6c0QeLyEci8oyI7NUT8ZgdV+tLCMm4umf/cYMYVdp5QjDGJF/Sb0wTkULgYeAqVa2KGf0BME5Va0TkROAxYEqceVwMXAwwduzYJEds4vnpU65gV2zPDTKmz0pqCUFEsnHJ4AFVfSR2vKpWqWqN734ayBaRsjjT3aWqM1V15pAhQ5IZsokjHNHoy+rLCnNTHI0xJlmSlhDEPd/gz8CnqnprB9MM99MhIrN8PFvjTWtS5+3PbJcY0x8ks8roUOAbwHwRmeeHXQeMBVDVO4GvAJeKSDNQD5ypqvZ4ml5m0UZX03fzl6Z1MaUxJp0l8yqjN4BOX1arqrcDtycrBtM9fvrUpwAcs6dd5WNMX2Z3KptOLSuvjnZb+4ExfZslBNOpr9z5dqpDMMb0EEsIplMVde4ZRtecMDXFkRhjks0SgulQy/uQAXYf3vED6owxfYMlBNOh15duAeDwKWUcPrnd7SHGmD7GEoKJa2tNI9+6fy7gnkKalWlfFWP6OvuVmzZWba2lsj7Eyq210WF59iJ7Y/qFpD/LyKSXI295pd2wok7ee2CM6TushGA6dec5Mzhyt6GpDsMY0wPs1M9ExXtqyBf2HpGCSIwxqWAlBBO1tbapTf9jlx+aokiMMalgCcFEPbNgY7R7ytBCpgfed2yM6fssIZiolvcmA8wcPzCFkRhjUsESgolqeW8ywJShdmeyMf2NJQQTVRsoIWytbUxhJMaYVLCEYKK21LQmAXvUtTH9j112aqJqGpqZOryIq4/djWP2sJfhGNPfWEIwUQ3NYQpzszh+r+GpDsUYkwJWZWSi6pvC9twiY/oxSwgmqiEUIS/bvhLG9Ff26zdRDSErIRjTn1lCMFGWEIzp3ywhmKiGZqsyMqY/s1+/AVzpYFttE3lZVkIwpr+yhGAAWFZeA2CvyjSmH7NfvwHgj68sA+CwyWUpjsQYkyqWEAwAT893j77OtTYEY/ot+/UbtgdejFMTeMCdMaZ/sYRgWL6lNtp94IRBKYzEGJNKlhAM0Pou5YIce7yVMf2VJQRDVYOrJvrVGfumOBJjTCpZQjBU+4QwfUxJiiMxxqSSJQQTfZeyVRcZ078lLSGIyBgReVlEPhGRhSLy3TjTiIjcJiLLRORjEZmRrHhMxxpC7l3K9hwjY/q3ZJ4SNgPfV9UPRKQIeF9EXlDVTwLTnABM8X8HAnf4/6YHNTZHAMjNsgKjMf1Z0o4AqrpBVT/w3dXAp8ComMlOBe5X5x2gVERGJCsmE19DyCUEKyEY07/1yCmhiIwH9gPmxIwaBawJ9K+lfdIwSdbQHCY7U8jMkFSHYoxJoaQnBBEpBB4GrlLVqp2cx8UiMldE5m7evLl7A+ynGpvDXHDvezy3cCN3vPIZ2fZQO2P6vaReViIi2bhk8ICqPhJnknXAmED/aD+sDVW9C7gLYObMmRo73uy45xduYvaicmYvKgegrimc4oiMMamWzKuMBPgz8Kmq3trBZE8A5/qrjQ4CKlV1Q7JiMq0+XF2R6hCMMb1MMksIhwLfAOaLyDw/7DpgLICq3gk8DZwILAPqgG8mMZ5+qzkcYWl5DeMHDyArU8jOzOCeN1ekOixjTC+TtISgqm8AnbZSqqoClycrBuNM/vEz0e5DJw/mgYsOSmE0xpjeyloS+5k3l22NO/zSoyb1cCTGmN7GnlWQ5sIRJRSO7NA9BK5gBidNG8H/nj6N4vwsXJOPMaY/s4SQxuqbwuzxk2cBWHnzSQl/ruXO5KqGECUF2UmJzRiTfqzKKI0t3lQd7W55HlEilvjP7Td2YLfHZIxJX5YQ0lg40npLxr/eXxt3mkik/W0bNz3pHif1yAfxP2OM6Z8sIaSxJl/1A1BVH4o7zb/jHPTfW7kdwO5ONsa0YUeENDZ70aZod1WDSwjhiPKbF5ZQWReiIRTmR//+uMPP33/BrKTHaIxJH9aonMZq/eMmSvKzqfFvPXtt6WZ+99JSVm2t5dg9h3f42dOmj2TMoIIeidMYkx4sIaSxhlCYUaX5rKuo54E5q6lpbKbZtxlsrW2iMlCN9O51n+eu15Zz9xvuDuXGQHWTMcZAglVGIvJdESn2zxz6s4h8ICLHJTs407mGUJi87NZd+Pi89Tz1sXsUVESVuqbm6LihxXlcf/KebT5rjDFBibYhXOAfXX0cMBD3jKKbkxaVSUhDKEJ+Tvwb0t5ftZ356yrbDR832FUTnTDN3kNkjGkr0SqjlttYTwT+qqoLxW5tTbmGUJi8rEzOmjWWB99dHTMuwuPz1rf7zCs/OArA7kw2xrSTaAnhfRF5HpcQnvPvSLZK6BRrCIXJz8nk51+axk2n7tXhdI9edki0W0QsGRhj4kq0hHAhMB1Yrqp1IjIYe1R1ytWHIgwa4KqMNlc3djid3ZFsjElEpwlBRGbEDJpoZ5e9R2OgUbkg1y4YM8bsmq6OIr/2//uy6eoAABfDSURBVPOA/YGPce0J+wBzgYOTF5rpSl1TmALfqHzU7kO4+ZlFAOw5ophPNrjXV39u6tCUxWeMSS+dJgRVPRpARB4B9lfV+b5/b+CGpEdnOlRZF2JjVQNFee5ppVOHF0fHPXb5oYQjyrqKekYPzE9ViMaYNJNoo/LuLckAQFUXAHskJySTiC/e/gYAq7fVRYedvI+7lDQnK4P8nEwmDy3cofckGGP6t0QrnueLyN3A33z/2bjqI5MiLYlg75El0WG3f30Gt389VREZY9JdognhfOBS4Lu+/zXgjmQEZHbM5KGFqQ7BGNNHdJkQRCQTeMa3J/wm+SGZROw1spiNlQ2cOK3jB9gZY8yO6LINQVXDQERESrqa1vScmsZmDptSZjeZGWO6TaJVRjW4doQXgNqWgap6ZVKiMp1SVTZVNVBo9x4YY7pRokeUR/yf6QUmXPs0ABV18d+SZowxOyOhhKCq9yU7ELPjNlY1pDoEY0wfklBCEJEpwM+BPXF3LQOgqhOTFJdJwH5jSlMdgjGmD0n0xrS/4C4zbQaOBu6n9Z4E08MOmTQYgKuO3S3FkRhj+pJEE0K+qr4EiKquUtUbgJOSF5bpzFufbWV4cZ41KhtjulWiR5RGEckAlorIFcA6wO6ISqFhxbmpDsEY08ckWkL4LlAAXIl76uk5wHnJCsp0bNFG9xTTAycOTnEkxpi+JtESwjZVrcHdj2AvxkmhZeU1ABxsCcEY080STQj3iMho4D3gdeC14NNPTc9ZvtndFzhpiNXYGWO6V6L3IRwpIjnAAcBRwFMiUqiqg5IZnGmvIRQGYJS958AY080SvQ/hMOBw/1cKPIkrKXT2mXuAk4FyVd07zvijgMeBFX7QI6p6Y8KR91NVDSEGFmSTmWHPMDLGdK9Eq4xeAd7H3Zz2tKo2JfCZe4HbcfcsdOR1VT05wRgMUN3QHH1LmjHGdKdErzIqA27EvUP5WRF5UURu6uwDqvoasG0X4zMxXl5UTr69Bc0YkwQJJQRVrQCW46p3NgCTgCO6YfkHi8hHIvKMiOzV0UQicrGIzBWRuZs3b+6Gxaav4vxsttclUkAzxpgdk1BCEJHlwK+BQbhHWOyuqkfu4rI/AMap6r7A74HHOppQVe9S1ZmqOnPIkCG7uNj0pgqHTSlLdRjGmD4o0TaEyaoa6c4Fq2pVoPtpEfmjiJSp6pbuXE5f09gcJs+qjIwxSZBoG8JkEXlJRBYAiMg+InL9rixYRIaLf92XiMzysWzdlXn2B/VNYWtDMMYkRaIJ4U/AtUAIQFU/Bs7s7AMi8iDwNrC7iKwVkQtF5BIRucRP8hVggYh8BNwGnKmqujMr0V+oKvUhSwjGmORItMqoQFXfjXl/b3NnH1DVs7oYfzvuslSToFBYiSjkZSeax40xJnGJHlm2iMgkQAFE5Cu4q41MD6r3dylbG4IxJhkSLSFcDtwFTBWRdbjLT89OWlQmrppGVyjLz7GEYIzpfok+y2g5cIyIDMCVKupwbQirkhibibHCP9huaFFeF1MaY8yO67TKSESKReRaEbldRI7FJYLzgGXAV3siQNNq/rpKAMYOKkhxJMaYvqirEsJfge24q4W+BfwYEOB0VZ2X5NhMjL+94wpkQ4rsbWnGmO7XVUKYqKrTAETkblxD8lhVbUh6ZKadvOwM8rIzGDQgJ9WhGGP6oK6uMgq1dKhqGFhrySB1FLvCyBiTPF2VEPYVkZZHTAiQ7/sFUFUtTmp0JmpjZQPLN9dy8j4jUh2KMaaP6jQhqKqdjvYCqspBP38JgM3VjSmOxhjTV9ktr2mgsbn1uYJj7AojY0ySWEJIA42h1oSwxwirpTPGJIclhDTQ2OweWbHXyGLOO3hciqMxxvRVlhDSQEuV0TcPnUBWpu0yY0xy2NElDTREH2pnu8sYkzx2hEkDizdVA5DZ9vHjxhjTrSwhpIGWRuVJQwtTHIkxpi9L9PHXJkVun72UXz2/BICBBfbICmNM8lgJoZdrSQZgD7UzxiSXJQRjjDGAJYReb9qoEgCuO3FqiiMxxvR11obQy40ZlE99KMzFR0xKdSjGmD7OSgi9XFOzkmM3oxljeoAdaXq5T9ZX0uAfXWGMMclkVUa93PrKBrIz7YY0Y0zyWULopQ77xWzOmjWW7EzhosMnpjocY0w/YAmhF4pElLXb67nlucUAFObabjLGJJ+1IfQyzy3cyMTrnm4zrCDHXlxnjEk+Swg9bHN1I5X1oQ7H3/fWynbDNlY2JDEiY4xxLCH0sAN+9iIH+/cjA1Q1hLjovrn8fc5qAJrD2u4z4Uj7YcYY090sIaRAXVPrZaRvLN3Ci59u4rpH5wNQVtT+AXYHThzcY7EZY/ova61MoQXrKrnsgQ+i/cfc+irLymvaTXfsnsN6MixjTD9lJYQetHJLbZv+1dvq2vTHSwbGGNNTLCH0kM3VjRz1q1ei/fe+uaJN6cAYY1ItaQlBRO4RkXIRWdDBeBGR20RkmYh8LCIzkhVLqjQ1R3ho7hqqGkLc8+aKNuNu+M8n0e6S/Ox2nz1ytyEATB1elNwgjTHGS2Ybwr3A7cD9HYw/AZji/w4E7vD/+4zbX17GbS8t5fbZy9pVDwV9ecboNgnjgkMn8JMv7kkoHIl71ZExxiRD0koIqvoasK2TSU4F7lfnHaBUREYkK55U2OTvHwgmg6uP2Q2A3Cy36fcdU8qZs8ZEx+8xopgjdisDIDszg3y7Kc0Y00NSeZXRKGBNoH+tH7YhdkIRuRi4GGDs2LE9Elx3yM1un28vPHwCv3lxCY3NEQDuPGcGI0ryWXnzST0dnjHGtJEWjcqqepeqzlTVmUOGDEl1OF1aVl7NwvWVlMa0Dcz+/pHtnks0tCivJ0MzxpgOpbKEsA4YE+gf7YelvWNufQ2AM/Yf3Wb4xCGFbfqHFeeSmWGPtjbG9A6pLCE8AZzrrzY6CKhU1XbVRemkoq6Jm55svXroyY83MHhADk9+5zCeuvKw6PDnrz4CgF+dsW+Px2iMMR1JWglBRB4EjgLKRGQt8N9ANoCq3gk8DZwILAPqgG8mK5ae8sCc1fz5jdarhepDYaaNLmHvUSVtptttWJG1GRhjep2kJQRVPauL8Qpcnqzlp8Idr3zWbtitX7VSgDEmPaRFo3K6qGlsbjds9MCCFERijDE7zh5u101cgadVQU4mPzp+9xRFY4wxO84SQjcpr25s03/HOftHHz9hjDHpwKqMusmmqrZvNcvOtMtJjTHpxRJCN6lucO0H+dnuURPxHlhnjDG9mVUZdZOWhHDP+QegKHuOKE5xRMYYs2MsIXST6oYQAKNK8xk72K4sMsakH6sy6iYtl5wW5VmONcakJ0sI3aSlyqjQEoIxJk1ZQugmNY3N5GVnkJ1pm9QYk57s6NVNqhtCFOXZlUXGmPRlCaGbVDU0W/uBMSatWULYAfVNYcqrGvjZU59w/WPzCYXdW8/WbKvjqY83UGCvuzTGpDE7pU3QwvWVnHTbG+w1spiF66sAOPfg8ew2rIiP1lYAsM/o0lSGaIwxu8RKCAlauM4lgZZkAHDLc4tZvrmGK/7+IQCXHTUpJbEZY0x3sBJCgl5ZUt5u2AufbGLxxupo/5Ci3J4MyRhjupWVEBIU83RrfnfmdABWb6sDYPCAHHKzrA3BGJO+rIQQY3N1I9+6fy4HTRzM28u3kiGQnZHBuyu3tZmuOOYS00utusgYk+YsIcR4duFG5q2pYN6airjjv3noeK46ZjeWlbdWFb34vSOYPLSop0I0xpiksCqjGPVNbV+DeeYBY9r0//cX96IkP7vNTWgl+Tk9EpsxxiSTJYQYNQ2tCWH/cQP56gFjGF6cB8DNX5oWHResMrLGZGNMX2BVRjGqG5spzM3ixe8dyfASlwjeue7z7aYrLXAJ4bTpI3s0PmOMSRZLCDFq/CMoWpJBR/KyM3nh6iMYNTC/hyIzxpjksiqjgFA4wjMLNpKTldhmmTKsiIIcy6nGmL7BEkLAG8u2UNPYTG6CCcEYY/oSO/IFVNQ1AXDTqXunOBJjjOl5lhACVm5xdx3vNszuKTDG9D+WEAK21zVRnJfFwAF2X4Expv+xhBAwb02FvfXMGNNvWUII2FzdGL2/wBhj+hu7ZhK49pH5PLtgA9vrQnx+j6GpDscYY1LCSgjAq4vL2V4X4vT9RvGV/cd0/QFjjOmDrIQA1IfCnHPQWH562rSuJzbGmD4qqSUEEfmCiCwWkWUick2c8eeLyGYRmef/LkpmPPFEIkplfYhSe2KpMaafS1oJQUQygT8AxwJrgfdE5AlV/SRm0n+q6hXJiqMr76zYSkShJN8ak40x/VsySwizgGWqulxVm4B/AKcmcXk7ZcG6SgBmjBuY4kiMMSa1kpkQRgFrAv1r/bBYXxaRj0Xk3yISt0VXRC4WkbkiMnfz5s07FUxDKEx1Q6jNsNeWbOZ/n14EwD6jS3ZqvsYY01ek+iqj/wDjVXUf4AXgvngTqepdqjpTVWcOGTJkpxb0+tItTLvheb51/9zosHPveTfanZ2Z6k1hjDGplcyj4DogeMY/2g+LUtWtqtroe+8G9k9WMBOHDABg7sptAFTWtZYWpo8pTdZijTEmbSQzIbwHTBGRCSKSA5wJPBGcQERGBHpPAT5NVjCThhRy8RET2V4X4vcvLWW/m56Pjpu3piJZizXGmLSRtISgqs3AFcBzuAP9Q6q6UERuFJFT/GRXishCEfkIuBI4P1nxAJw23TVh/H72MiIKfzx7RjIXZ4wxaSWpN6ap6tPA0zHDfhLovha4NpkxBO05sphRpfmsq6gHiD6m4swD7O5kY4zpd3cq/+D43Xh+4Sb2HVNKblYmK35+IiKS6rCMMSbl+l1COH2/0Zy+3+hovyUDY4xx7FpLY4wxgCUEY4wxniUEY4wxgCUEY4wxniUEY4wxgCUEY4wxniUEY4wxgCUEY4wxnqhqqmPYISKyGVgVM7gM2JKCcHZEOsQI6RFnOsQIFmd3SocYoXfHOU5VO31/QNolhHhEZK6qzkx1HJ1JhxghPeJMhxjB4uxO6RAjpE+cHbEqI2OMMYAlBGOMMV5fSQh3pTqABKRDjJAecaZDjGBxdqd0iBHSJ864+kQbgjHGmF3XV0oIxhhjdpElBGOMMUCaJwQR+YKILBaRZSJyTQ8t8x4RKReRBYFhg0TkBRFZ6v8P9MNFRG7z8X0sIjMCnznPT79URM4LDN9fROb7z9wmO/EGHxEZIyIvi8gn/p3V3+1tcYpInoi8KyIf+Rj/xw+fICJz/Hz/KSI5fniu71/mx48PzOtaP3yxiBwfGN5t3w8RyRSRD0Xkyd4ap4is9PtknojM9cN6zT738ygVkX+LyCIR+VREDu6FMe7ut2HLX5WIXNXb4kwKVU3LPyAT+AyYCOQAHwF79sByjwBmAAsCw34JXOO7rwF+4btPBJ4BBDgImOOHDwKW+/8DffdAP+5dP634z56wEzGOAGb47iJgCbBnb4rTf67Qd2cDc/z8HgLO9MPvBC713ZcBd/ruM4F/+u49/b7PBSb470Rmd38/gO8Bfwee9P29Lk5gJVAWM6zX7HM/j/uAi3x3DlDa22KMiTcT2AiM681xdtdfygPYhR11MPBcoP9a4NoeWvZ42iaExcAI3z0CWOy7/w84K3Y64Czg/wLD/88PGwEsCgxvM90uxPs4cGxvjRMoAD4ADsTd5ZkVu4+B54CDfXeWn05i93vLdN35/QBGAy8BnwOe9MvtjXGupH1C6DX7HCgBVuAvZumNMcaJ+Tjgzd4eZ3f9pXOV0ShgTaB/rR+WCsNUdYPv3ggM890dxdjZ8LVxhu80X2WxH+4MvFfF6ath5gHlwAu4M+UKVW2OM99oLH58JTB4J2LfGb8FfgREfP/gXhqnAs+LyPsicrEf1pv2+QRgM/AXX/12t4gM6GUxxjoTeNB39+Y4u0U6J4ReSV3K7xXX8opIIfAwcJWqVgXH9YY4VTWsqtNxZ+CzgKmpjCceETkZKFfV91MdSwIOU9UZwAnA5SJyRHBkL9jnWbjq1jtUdT+gFlf1EtULYozy7UKnAP+KHdeb4uxO6ZwQ1gFjAv2j/bBU2CQiIwD8/3I/vKMYOxs+Os7wHSYi2bhk8ICqPtJb4wRQ1QrgZVz1SamIZMWZbzQWP74E2LoTse+oQ4FTRGQl8A9ctdHvemGcqOo6/78ceBSXZHvTPl8LrFXVOb7/37gE0ZtiDDoB+EBVN/n+3hpn90l1ndUu1O1l4RppJtDaGLdXDy17PG3bEG6hbWPTL333SbRtbHrXDx+Eq0sd6P9WAIP8uNjGphN3Ij4B7gd+GzO818QJDAFKfXc+8DpwMu5sLNhYe5nvvpy2jbUP+e69aNtYuxzXENjt3w/gKFoblXtVnMAAoCjQ/Rbwhd60z/08Xgd29903+Ph6VYyBWP8BfLM3/n6S9ZfyAHYpeNe6vwRX9/zjHlrmg8AGIIQ747kQV0f8ErAUeDGw0wX4g49vPjAzMJ8LgGX+L/ilmwks8J+5nZgGuARjPAxXnP0YmOf/TuxNcQL7AB/6GBcAP/HDJ/ofyzLcQTfXD8/z/cv8+ImBef3Yx7GYwNUa3f39oG1C6FVx+ng+8n8LW+bTm/a5n8d0YK7f74/hDpS9KkY/nwG4kl1JYFivi7O7/+zRFcYYY4D0bkMwxhjTjSwhGGOMASwhGGOM8SwhGGOMASwhGGOM8SwhmJQRERWRXwf6fyAiN3TTvO8Vka90x7y6WM4Z/qmdLweGTQs8KXObiKzw3S+KyCmSxCfzishpIrJnsuZv+rasricxJmkagS+JyM9VdUuqg2khIlna+pyirlwIfEtV32gZoKrzcdfbIyL34u5d+HfgM090V6xxnIZ7AN8nSVyG6aOshGBSqRn3DtqrY0fEnuGLSI3/f5SIvCoij4vIchG5WUTOFvduhfkiMikwm2NEZK6ILPHPJGp5oN4tIvKef3b9twPzfV1EniDOwVREzvLzXyAiv/DDfoK7CfDPInJLIissIueLyO2BdbxDRN7x63KUuPdtfOoTSctnjhORt0XkAxH5l39GFX7dP/Hr8SsROQT37J1bfIlkkv971j/w7nURmRpY9p1xts9eflvO8/Odksh6mb7BSggm1f4AfCwiv9yBz+wL7AFswz324W5VnSXuRUDfAa7y043HPc9nEvCyiEwGzgUqVfUAEckF3hSR5/30M4C9VXVFcGEiMhL4BbA/sB33RNHTVPVGEfkc8ANVnbvDa+4MxD3D6RRcyeFQ4CLgPRGZjrsb/nrgGFWtFZH/Ar4nIn8ATgemqqqKSKmqVviEFi2RiMhLwCWqulREDgT+iHseU0fb5xLgd6r6gH+4W+ZOrpdJQ5YQTEqpapWI3A9cCdQn+LH31D+GWEQ+A1oO6POBowPTPaSqEWCpiCzHPU31OGCfQOmjBJgCNOGeQdMmGXgHAK+o6ma/zAdwL0p6LMF4O/Mff0CfD2zy1U2IyELcAXs07uU6b4p7qVYO8DbusdoNuNLJk7hqojZ8SeIQ4F/S+kKu3MAk8bbP28CPRWQ08IiqLu2GdTRpwhKC6Q1+i3tBzl8Cw5rxVZoikoE7ELZoDHRHAv0R2n6nY5/LorjnznxHVZ8LjhCRo3CPY+5pwdhj1ysLCAMvqOpZsR8UkVnA54GvAFfQeubfIgP33obpHSy73fZR1b+LyBzcA9ueFpFvq+rsHVkhk76sDcGknKpuw72S8sLA4JW4Khpw1SnZOzHrM0Qkw7crTMQ9VO454FJxjwdHRHYT95KWzrwLHCkiZSKSiXvD1as7Ec/OeAc41FfnICIDfMyFuAevPY1rg9nXT1+Ne20q6t6BsUJEzvCfFRHZNzDvdttHRCYCy1X1Ntyb9vbpgXU0vYQlBNNb/BooC/T/CXcQ/ghXx74zZ++rcQfzZ3D16A3A3bhG4w9EZAHutYadlpR99dQ1uHc2fAS8r6qP70Q8O8xXU50PPCgiH+OqdKbiDvpP+mFv4N75DO6RzT8U90ayScDZwIV+Oy4ETg3MPt72+SqwQNyb7PbGPUbd9BP2tFNj+iGJfzms6eeshGCMMQawEoIxxhjPSgjGGGMASwjGGGM8SwjGGGMASwjGGGM8SwjGGGMA+P9uohq0zKfW2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reward function value curve\n",
    "plot_results(log_dir, \"PPO 3x3 Random Road Env Greedy Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zSNMlPjLhSvv"
   },
   "outputs": [],
   "source": [
    "# outputting the logged reward values\n",
    "!cat /tmp/gym/monitor.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UCz8DeTjR4Fg"
   },
   "outputs": [],
   "source": [
    "env_simple = SimpleGovernanceKernelWrapper()\n",
    "# logs will be saved in log_dir/monitor.csv\n",
    "env_simple = Monitor(env_simple, log_dir)\n",
    "# training sample environment policy for checking the env functionality\n",
    "callback = SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir)\n",
    "\n",
    "ppo_model_5x5_simple = PPO(\"MlpPolicy\", env_simple, verbose=0)\n",
    "ppo_model_5x5_simple.learn(total_timesteps=120000, callback=callback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "id": "A5pTgiXqR3-Q",
    "outputId": "4adddcd3-4e35-43e5-a27e-3a866a22aed5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helper function from the ploting library\n",
    "results_plotter.plot_results([log_dir], 1e5, results_plotter.X_TIMESTEPS, \"PPO 5x5 Road Env Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "k1EsmzEJR3zi",
    "outputId": "b378f247-8d26-4fb2-9c71-15312418c4e8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reward function value curve\n",
    "plot_results(log_dir, \"PPO 5x5 Road Env Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Cwvpg9oQQ0Hx"
   },
   "outputs": [],
   "source": [
    "# outputting the logged reward values\n",
    "!cat /tmp/gym/monitor.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "35FDVkHAQvtW"
   },
   "outputs": [],
   "source": [
    "env_simple = GreedyGovernanceKernelWrapper()\n",
    "# logs will be saved in log_dir/monitor.csv\n",
    "env_simple = Monitor(env_simple, log_dir)\n",
    "# training sample environment policy for checking the env functionality\n",
    "callback = SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir)\n",
    "\n",
    "ppo_model_5x5_greedy = PPO(\"MlpPolicy\", env_simple, verbose=0)\n",
    "ppo_model_5x5_greedy.learn(total_timesteps=120000, callback=callback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "id": "Vut_onVrdRmy",
    "outputId": "74073be9-398e-4766-81c0-aca06ba0787f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helper function from the ploting library\n",
    "results_plotter.plot_results([log_dir], 1e5, results_plotter.X_TIMESTEPS, \"PPO 5x5 Road Env Greedy Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "VZSaGoeVORy4",
    "outputId": "d1fed3a9-3da5-4f75-8a62-a37b509af2ab"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reward function value curve\n",
    "plot_results(log_dir, \"PPO 5x5 Road Env Greedy Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3Az2WnNvOR1w"
   },
   "outputs": [],
   "source": [
    "# outputting the logged reward values\n",
    "!cat /tmp/gym/monitor.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ofbHPLu2NtVb"
   },
   "outputs": [],
   "source": [
    "env_random = SimpleGovernanceKernelWrapper()\n",
    "# logs will be saved in log_dir/monitor.csv\n",
    "env_random = Monitor(env_random, log_dir)\n",
    "# training sample environment policy for checking the env functionality\n",
    "callback = SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir)\n",
    "\n",
    "ppo_model_5x5_random = PPO(\"MlpPolicy\", env_random, verbose=0)\n",
    "ppo_model_5x5_random.learn(total_timesteps=120000, callback=callback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "id": "uBJSEYRtSUcT",
    "outputId": "59633cb8-1a34-456e-b467-1e253b578d01"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dgtMIfF8I0QRsRJmq/iil3HxMS+bAgQMHDhw4cHCYmNJEJaX8Zynle4AlwB+BzwEvH+uCOXDgwIEDB291vNw2yDU/3MDLbYPHuygO8jCdODi3CSGeAH4DLAT+EWg41gVz4MCBg79kOILz7YG7n9nFc7v7uPuZXcDx7TdnzORiOk7GlwNVwDPAL4BfSSm7ppO4EGK/EGKLEGKzEGLjEZTTgQMHDv6ikC84jybeaYJwsvroaw9uaD8mdb5pTQtnN1dz05oWYGb9drT6Qadz52Pbj9mYeTtiOiaqk4E1wIvA+4AtQojnZ5DHuVLKVimlEzvHgQMHbzscLzKQLziPFl5uG+TGn7w0pSA83HofK0IxWXkmIxX62l1PvX5MhP8p8yr46UdXAXDNDzewdln9tPttKjKUX+eJ2lang5SHNWbs+UzV7zMZF8eaXE6FKZ2MhRDLgL8CzkEF+OtA+eI4cODgLxgvtw1y9zO7uGlNC6fMqzjexTlmMMIDjCA72rC35c7uEe566nU+d96iaeVXqB/0b2uX1fPk1q6ca3c+uo3BaIqQz20EYaE07PW+aU3LtPtaP7elM8xgNAUcebtpUjZReroea5fVc80PN+SU035Nt8XRhG674XiazR1DDMdSlPq903pWl8VeJntf5PeBbgN72960poXhWIrm2hIQYso+mqqvgUnH+2TvQ37adz66jc0HwrzSPshIImPaZqIyTjSn2H/XZZjOWJzOLqqvoQjNt4GXpJSpaTyjIYHfCCEk8H0p5T35NwghPgZ8DKCxsXEGSTtw4OBoYqaERU90U01aR1qGo0mkppuW/b58ITTT8kznfrvQ0MLrrqde56pVU8+J+tlX2gc5oTbEuguXTE4yhADghNoQoLQOWjjrempyZP8+XZJ3uIRisna6+5ldDEZTVAS8BdM7ZV5FDgGwl9OuYSnUntMRqpP1852PbWdzxxANFX4qAl4iyQybD4RzyqDx4IZ2Q16vWtWYU7Z8ogRj5GU4nubOx7abNvjceYtM2979zC42HwhTEfCyu3eUu5/ZxU8/umrCd6lQG61dVs8r7YN0heNcf2aTybsQdJm6wnHe9y9/ACDo8+SMO5O2NdbqyvycUORmz6EII4l0wbaxt+VwPM0jnzrT/D4TAmbHdE4Tv1AI4QcaZ0huAM6SUnZaJ5E/LYR4XUr5XF769wD3AKxcuVLOMH0HDt7yOBYC8VikMVNNhZ4Ah+Ppw1rpT7cMR1ODMt208u+z3ztTzYadbNx77akF77OTKK3B+eDKueO0EYVw05oWQ2Q2dwzlrHQLkQwtiOzEpbWhjLObq1m7rH6cAHy5bZDhWIrWueWTkhX7mNPPPbm1K+f6nY9uAyFYd+ES0za6jHbBnt83a5fVs6UzzOfOWzRpO09GgiZCfv/o3wqRvoJ9IZXYCkeTjCQyzKsM5JiJ7O1y11OvMxhN8dXHt+do1uzEQ/eFvlbq9+b0kf5dk7X8vl67rJ5Lv/N8QTIxURs9ubWLkUSGkd5RntzaNem7ocukSZzGdT/aQF2ZP2ec5I+1kUR68v6x2hIpc8bLFSvn5tQ1//NEEFJOzimEEBcB3wCKpJRNQohW4ItSyounTD03nduBUSnlNya6Z+XKlXLjRscX2cE7C9f8cAPP7e7j7ObqaQnomd5/tNI4XGJVSKVeEfBOKMxnWga7ueWhl9rpiyQJR1N8/oLFnFgXGqe6zhfq9vSmqyEqZOLJT3/tsnq++vh2RhIZWhvKeOTTZ025atYCajrq9pn04cttg9z6P6/SPZzg8xcsztFUaEERSWYI+jxcsXJuTp3s1wA2dwzhcQm+eMkyrlrVaMrR2lBGqd/LkvpSfr6xgw+unMv2rmFT9ku/8zybD4RprglSX+43BEGn9eTWLkMMPS5BU3WQ3b2jhHxuRhIZAl4XwWIP/SNJ5lT4+dcrV4wT/vntp6/b67DuwiUFx92DG9r56uPbKS5yE4lnmFPh52sfOAnApF8R8FJd4mN37yjNNUHVJpYWYnPH0IT528fFkvpSfvynfaSykovfNZvf7ugxY+SKUxu566nXCfo8HBiMjWurQu9N/piaaGze+j+v0jkUJyMliXQWwGh7fvz8XrqHE1y9qtH0mX08//j5vbQPxABorApw/ZlN5l3rCas0L37XbAYiSfMe7uoZJZrKEPC6cbuEIVP54zW/f1Y3VfLzjR187rxFOe9v/vupzVsAXrfgZx87fcL6r5xf+XIhP9/pEJyXgfcAz0opV1i/bZFSLp/iuSDgklKOWJ+fRhGjJyd6xiE4Dt7umKmJ5ViZZCaaFI+1v0y+MMpXv+vJMX8VP51yaUGrEfK5WdFYYUgAYMiVzl8LDreAOy9dzlWrGse1xWREQgttLdzt99nLE/J5uO+G0yYkePbVaCSRZnfvKA3lxXQPJ0hn5aSCU2t1vvr4durKxoRyvlDXeefX49Lv/sloIgDcAjISk+d1P9rASCIDQGtDGXsOjRqBvO6ipabcMEZ+0tkxudFcW0J9WTFd4Ti7e0cJeN1EUxmaa0vY1xchnZWmPe58dBtbOsNkpCrH8oZy+kYTHBiMjevv5pog3cNxkmlJIpMl5HNz3w2ruOlnr3BgMEZDhZ+7r1yRo3FqbSgjkkgXJHorvvgbc5+G7te1y+qNdkUTrta55ZQWe3hudx8hn5u6Mj8A3eE4I4m0eTa/v15pHzLC3g49RgA+cu96oqmsaSs9Fm48q4lbL1g87lk9huyaJfs412PfjoDXxf03rs7ROIZ8brbcsRYYe590OrlldZsxMb4ebjM+dP13do/wpce2AYLbLlyS0+7296S1oYytB4dJZyVuAT6Pqr/PLSjyuHMImDZXaTRU+FlQHcwhzvrduf/G1QUJznR8cFJSyrCwBriF6ZiSZgG/tJ7zAA9ORm4cOHgrYzKnTTtmak6Z7v3TISj59xQyq9j9NI6Fj8sp8yq499pTTVtpE4s9fz1x3vnoNtoGosZp8t5rTy0oyHXZtJ9APJ0llZFksuT4iWhoDYNemYIS6NqnJb/NCzl6glrxb+lUK8hI0iIAlvr9a4/v4PndfXjdApdQq9frfvQin79gsTEXaZW91ppo4hDwqs2rXeE4GTlW5nzTkL0Pb/zJS8aEcOv/vErQ58kxEdz56DZAkQ27bwRAZx55yEiMg/GtD7+WI8h2dI9QG/IxkoiBEErIHAjTOrfckCjIFaZ7D42yu3eU1oYymmuC7DlkncUsJU1VAbqHE8a09Minz+LBDe184VdbSWclpcVKBNkJjkAJs+7hRJ6QFezsHjH3dg3FjMnFLaC+3J9jlvnSY9uN5uLzFyzmc+ctYt0jW0ybA0YLMRxLUR0sIp2RRshqMh7wWsI+HDPlqQh4QYiccaTHlc+dIysBReZGEmnTftGU0rDMKS+mvtzPK+1DpLOSe5/fR2NVMGcRkK/FCvnctM4tN5qQymARv93Rg9ctSNkq11JXyinzKli7rJ7nd/eRRfnCaLLdF0kS8nlYPqeMP73RR1VJEaPxDNFUhtFEBq9LkJXStJfHBekslAWK8LjTrF5QxfauYWNS1XV6aGMHJ9aFcsxLXUMxuocTRJIZQ44zEqIp1Z6JjCSRSfMfz6n3dW9fhOpgESU+N6NWmx8YjHFgMIYAXAJDnG9a08L941rcGjHT0OD8EPgtcCvwAeAzgFdK+YlJHzwMOBocB282ptKgAAVXTROZYAoRofxVvd3HYv3e/nGmA52XXsFqIZmvwp5IE9FcE6QvkjSOjLpc+Sv1/JXrRBqXiQhd/j3599o1I1rlvKN7hEQ6i0vACTUl7O4dNenZV3dAzsq0taHMkKGQzw0oUlFI66LzbSgv5sBQHFDxME6oLeFrHziJp7d1c+/z+7jwpHoGIskJTUV6xe8WECjyGP+Be689lQ9+/wVTTr0K13VYd9HSHLIC5KySGyr8RBJpzmmp4dFXDxrCMZLImPTt5QFlfthtEYeQz8MJtSVs7hgy9QIsc4+noPbALcDjdhnTRUOFH7/HZdK0I+B1s3J+RY6ZIORz8/kLlvDk1q6CGgpdbl1vj0uwbE5ZzpjVddLEwW6yeGBDG5FkhqxU5quff/x0nt7WbQSe1jrZtUfanNQ5GDOmkmgqg0CtwANelxG6WnOhTXl7+yJGcNs1CboPS/1e8x563ZDKKKFa7HEzp8JvTDh2EmInbjodAIQYZ5axC39tCuoaiuWMBWCctlC3g37H7WMs5HNT5vdycCjOnAo/f//uhTy5tcto1rQW024qtENryK74jz/nkECNhgo/1cEim2lKtW/+mLNrO/O/B7xqDOanr/tMwwVkmVyTBPCJsxdw6wWLEUIctokqAPx/wPutcjwJ3CmlTEz64GHAITjHF8fKjHEk5pLpmHem0qpMhnzzRL6znxaqekL54Mq5/HxjRw4hKFTGfOGu02yuCY4TKnbSpPMt9XtzBIm9LEbdb6lwtYlAt8PG/YNmsm62hLrdRyLgdeF2uRhJpI3A8LgEN57VZNTDdlI2EfmxO2Hm1+Pea0/l6W3d/OCPe5ldriZbTaQ07ESlIuBlXlXQmJMW1JTkaHD29kVyVvn55gH7mLjpvzbRORTPmTB12fKFGSgh0jUUY/ehCD63YPHsMq5YOZdvPb2TQ6NJ86xLQFYqLc78qgCPbD4IYGlkhDHLBIvcRuMDGPL60EvtdIZjHBpJWs+5AcxzSGm0DQ9t7GBzx1COmUT329WrGnnstYOGvPncLlKZLFmwtAeWBkFgCI1u70xW5TcREQIlPJ/+7LtNe2pirIWrnTjottW7erT2TAvyQu9SvklEf2+uCbKvP2rMDqDIrR6bD2xoyymHvV4jiYzpH/v78KXHthFNZc17YCeNdz66zQhrnY7eWba5Y8iYpewkHCztUjieY3qzE3qdjt3XSb9P+pomRXbzjfbH0YQElO/J/Kog15/ZxPd+v9v0eX7/CcDvdRvzkL3f7ORB95UmmNrcputVHSxiR9cIiUzWGluCVFaaca/Hoh53iUyW5pogh0YTDMXS+D0CIVwUeQVD0bQZ57dduCTn/dd95XO7SGWzZCXjyjkYTVEbKiJYpPyVslKVw/5e+9yCJbNL+dXN7389m4iOs+1NSXDGPSDEicA/Sin/bkYPTgMOwTm+OFLn1uk4WNpXI4Wc0fI1AoW0C/nltduE1120dFqkZyJypP0VtJDVKx8tFPO3aBbSVBTSauj/81e/ehLNd767alUjD25o50uPbSOVkbiEoKbURziaMoRAO0Pm28TzyYB9BX3no9uMGt9Obuw+FXo3zZce20YmO+Z0+OPn99JpTbBKSLrNqlsARbbJ6uzm6py66glLkwF7GnrCt8d/0fXX3+94dNs4x8n8XSj5Amsi+NwuMlKaun/xkmV86bHtOc/pCRjGVpNasOuV+eYD4ZwVdedQjHg6a56rCSmTh90Z1675MXmhtDBa2FQEvMyrDBiH3aDPY/rM7lc0FWpCRQxGUjn5Bb0uakqLWVgbYsPePkYTGYq9LmKpLF63wOdx01QdxOMSbOkM43ELkilFnmBMCJUWe0imM9SEirli5Vy++/vdxNOSgNfN/TeuMmNtNJFGSjg4FCOWVqv9z1+w2JiK3AK+eMkyHnvtIO8+sZZfbjrASCKDxyXwugVd4TiRZIZT51cQTWbYdnAYAVQEvaQyWUbiithIS/CV+Nx8+j3NZLKSZ3f20tpQzn1/3oeNj9FcW8I1Z8ynYyDKT/+8n3g6S7HHxaqmSv68p19t5LFMIPlkrhDK/B4+fvYJ/HLTAaIppZU6p6WWR1/tpHs4gccFLpcgmc7t9/cuqsXjdvHUtm4Aijwu43y97eBwzr3zKv20DYy90zec2cRDG9uN+Ub3S5Fbac52dI0Qm+A9EFZeS+pL6RtN0DkUo8jjQsoxQiyAULGHIo+LvtEkLgFetwsByjwsJUVuNW8EijxEkmkmoxJzyotZOb+SJ7Z0kcxISnxuqkt8xJIZekaUrkS/Z15rPpLkvoeT4eC9fx9J9rWXjKvrRARHCHESavfUbOAR4LvAd4BVwDellN+aOtuZwSE4by6O1BE135RjX63p1XW+eQYouFXUfq8WuHaNgBZodkKiBWKJz0PHYCxHdW8X+trub3awMM8AACAASURBVBee9p0hgFEX610xkKthAHKc2iYiZvmETBMmvaqsDBbx2GtdxjxiN31pDUZFwGs0RZrEaNi1L7rseldRecBLh+V8OTCawFqEkchkTTvYSWFZoIhwNMXVqxq59/l9pLMSvf532+z5hdTK9hVYIpMdt/q699pTjVnF5xY0VgVzdukUcmbVK04t1LUzqsclcAtBIqNMW//9iTPGjat8h0SN8oCH0XiGC0+q54ktXSQykobyYj55brMRsvnq8XxUBrzMKi1m2exSHnvtIFkpkEiSGUldqY9IIk2Rx01/JDlJKkr4ACQL6f+BYo+LuCVg8klnic9NIp2lPFBE30jClNftEmSs+9wugbRWuTOBAAI+NwuqS0hnsnQOxRiOj9fuTNVOGpMJJZ2GEEwqEB0cHlwCTp1fydaDYSKW9sbndeH3uomlMsTzyJrPo97tcr+XxbNLeWlfP+msGktlfi9D0SQuIRACMx+4BJT4PDRVBykPFLHlwBAD0RR1pT6WzC7jd6/3zqjMU42r+VUBuoZiZKUap8lUlotbZ/Pk1m7C1jg9+OPPZJI9e8b5FE9GcDYA/w68AJwPfB74CfAFKWV8RjWYJv7SCM6RmoSONL6K3Yt+qq2JhWAnCXb1sxbUdt8LTSDyCYC9DJ87b5EhGFrV7nYpr/yHXmo3vgBaKGoB2VxbQt9owmgI3C4X/iIXvSNJo5rWdmUXcJJlYnj01YO4XGOCvJAZI58A5WtltKoXyFH3Kg3GmEOshl6l6F0D711ca/ww7CYLrRkI+dzUlRazpy9ihEYhAgnj7dUBa2UuUaruWaXFDIwmqSwpMpogUKvoMxdWG0dEOwqZdGBsUmooL2ZBTQkb9g4YtXZtqIgij5tDwwncLkFVSZEhoFevauRnL7Vz2YoGHtrYTiSZpb6smKFoklgqm5O2HTWhIkbjaVpmhegZjlMTKmZH15i/TpnfQzim6lPscbFmySxeaRskI2F+dYBDIwn2HopgLc6hQB7HA9pJOZXJGi1EIXhcAo91b9Rm/nILQcaaw4u9rnECTKDIhB47gSI3/3zREs5fXs9H7t3Aq5ajcv77aNdcBLxuKkuKcrSCTVUBfnjdqVz2vT+Zds+HQAnKfI3VRKguKcLncSOl5GB4vIiZXxVgUV0pz+7sIZ4en+b8ygButxhzcp4AhQhY/phzuzALBICmKj/tAzFD8n1uFxVBL93DuZ4aJT43WQnRZAa/RxArUM7pwu9xEbOZGH1uwdf/9l1893dvsCvPbFbm97DxtvfhFgKXSxTcEq99lQrV99LW2fzrlSvGOTPr3WR7ekeMuavE5+G9i2v5w65DnNNSw683HySL0ow9ffM5xhQOY35IEvjAyQ3c/8J+PG4XV62ax0/+pBzAK4JqfvB7XJxYX8rqpkp+8Me9NudmwRknVI1zJbj1f16lfSBGKpul877/l0TX7nHe3ZPtovJJKe+zPu8UQnxGSnnLdDvnaOPN8g95MzHTHTf5gY/sNs2Z7NjRkWd18KzBaMpEv9T5FIp2mR+oyx7oTb8Q86qCrG6qZE/vCF16khKCzR1DbDkwREZiPN91ALGQz8NgNMWTW7s4oTbE5o4hEhkJSMhgth+C2gWwoqyY4Xh6TJhLybzKAOmM3kGRZcQ273zk3vXmZclCTlky1gWfW5BIZc2KZk6Fn/qy4hyfgqtWNXLVqkajlZloOyiM37KpoacrvWtAkxvAbJcE1UYZCf/4/hP50GmNbGof5Jb/fpXekQTzq4P8YdchdnaPsHZZPRv29pPKSrNlWsOuWk9lpBFQ8cEYWcYmuYykILkpLfZwVnMNz+8+BJbLhDZFaTIzmsiwq2cElxuwuqN3xKbJyEDMynckMbZL4sd/3m9u6coTZoVEgvZb0QK5ZySBfTazC9l4Ostjr40FmBuMJklnsibdmYicxkol2PL9PgBWzC2nOxyjZziR03aVQS/JVIbiIjfD0TQSSNkkqvaXiKYynL6galomp3RWmdTyd+hkpDQ7iLqGxghIQ4WfA4MxXHaibG1zvveP+/ivFzs4fUEVew+NUlfmN+/ykvpS/rynn/cvrWN/X8TMNV/41dacfMsCRSyoKZmQ3IBq56bqILFk2pg2dSvkO5QD9I0mzaLoqnteIJGRuF3gc7sJFrvpH01ydksN0WQ6Z5wHvG5a6pSZU+8m81o7fux5TkTW7fdoVAaKGIgkjRmtZziZ0/+JTJZDtklGjw+B4P6PnmbMpZDJCQeg0dpQxo6uYWueG0tDiLFyx9K5mtFERhJJZMx3AVSHiogns5x7Yi2nffkZM089tLFD7bqz5kgdliA6pL7780xvj73WxUdOn8+dj26jusTHvKogkUSakd5RkJLPX7DELD5HEmnjf6b/B4ilMlz6nefpiyQJeJUzdiSZMRroLZ1hstb8//CmA7zRFwUgmoyZ+pYWe1i/b4CMxOziuvGsJt63tM5EdNbaeLsfl8xmC9rjJjtss1gIsUIIcbIQ4mQgkff9TYUWznqb3Vsp3ZkcPmZ/ZjrRQe24+xkVkntzxxBffXzHlFE788t10xp1eF8kmeG53X089FI791576rjD2SaKdqm3jG7uGOLGn7zEzu4RAK5YOZfmmqAKUd4xxAMb2hlJZEzYcK2L1uaARCrLzu4RUx+Qph2uWDmX/F2W0VTWeO13h+MsqS8FKWmuLaF1brkiUAfClAW8hHxuXNbzboG1arGtgjxqyNeX+81vLpQAiaYypC3B3TkYZTie5oEN7SZs/oMb2lmy7gletYRRmd9j8rLDLdTKPB/lfjd+jyvnWv5KUk94fRH1Mn/58R0s/sKTfOgH69nbH2U0meGRzQcZjKb4P7/cwhd+tZVERpklnn+jMKnKh24NWeA3O4bjaR7f0pVjrpBgyA3AUCxF93CCWHLsN3ubCDFeeHjyZp3LWmebiUigJrZCaJlVwklzSlU55MQmDt28LuCE6iCNlQEjnFyo1bfOy55Vc02QkM9NeWBs3edzK78ILegaKvyELFPbG70jdA0nmF1eTMjnNumGoylGk1n6RlMks5KUzfQ3t8LPu+aWG3+utcvq2dM7Ysqmy+NxCTW2bWiuLcHtGms8t1DOphkJkUTamPO+ctlyPvnuhabcHpfg8xcsob6smN29aku3/T3tGx0T1A9saCOdlfxmWzelfq9ZSGl/pUtbZ1MR8JrIspe2zi7cCRa6wzGqQ8VYyxU8LsEnzl4AKPKjtZYuq35rl9Vz9zO7uP7MJkUaLKfowYjSOK57ZAtd4TjNtSXmWbcL47R7xamNVAS8VASLkMCcCj+tDWWmnVpmlXB2czWfOHsBAa8Ln9tFQ4XfpKWhyQ2oeWtOeTEVAa+ZP/TvGtIS3FdbYQgQwvRxdajY+PMEvG6aa0u44tRGGisDhHwe5laouWh5QzlfvGQ5IZ+bhgo/buvdsb8udz31+liewJzyAPfdcBqPvdZlIiRf88MNRPIWXi4B/iI1bpVGzpcz9i88SUWw3nwgzO7eUSLxFN3hGM21Jay7aCkn1oXwuMfTBftc1jUUY/OBMAcsTVGwyE2waKxd9Q65wWiKPb0j5j0rC6hzu9zCCvtgvdhLZ5ey56t/za0XLB6LntwxZMiN116cbKagfdh9++23F/qdO+644wPAeba/Udvn999+++0/KfjgEeCee+65/WMf+1jBa41VQbrDMW5a08Jsm3A6UjRWBdnVrZh086wQs8tVnIBP/ufL/OylDvObxsttg9z68Gs0VgXN77c+/BrP7e6jOxzjspMbpsxTa0j290dpmVXCp85dOO66zj8r1aBurAqyekEVj73aSSorqQ0V0Tq3nK9cflKOQ6+9bLpcu7qHeXxLF6sXVPGpcxfyi02dZvvxze9r4bKTG3h25yGu+dEGXu8a5s97+phXFeRT5zbzwPo2GquCdIXj/Pj5vSQzarKOpbL8fmcv+/ujpDJZ9vdHjYnB44L5lmC5pHUO2w6GGY6lzSo2lZW8uK+fYJGHiLXKCPnc/Of6Nv6w81COj0KOqBMq31faB+kaTrBsdimXtM7hF5sOIFECOZlRE3Gxx43H5Rq3WltQFUACl6+YQziaJJWR1Jb6GIqlcQv4u79aYExf3cNxakNFSCmpLS3mya3dxhzlttTc9l0qLpSaPZ2Vpp3siKfVKny6PhJSQmmxF5dgQkdHe1r2ZF1C2atTGYnHNTa5rF1WT4nPQ/fwmNbE53Gxan4F/aMJ5lT4WVQX4oClDVhgOZzazSKgNF7apOL3uIxjYHNtCZW2XTJet8gpY8jnxut2k7SRpNe7R3LKHixy43GJHK0HKF+Yc1pqeaV9kCKPy6yIm2uChGOpcRoa/X9NqJju4bjqMyBQ5GJeVZBkOksincUtYGFtCV/7m3fR1h9lZ4+K6dIyK4REcDAcN2ShdW4515/ZxKb2QbOVOJJIM7cySO9owqzkXUJvVXar3R9WWVrnlnPL2kV0h2N85fKTeGB9G7t7I6a8C2uCpDJZ5lYG+PDqebx2YMi0/bLZpdxw1gJe2NOH1+3ihjObiCbT1JX5+djZJ5DKZPnGFa2ct7SOu556nX39UVOe/tEEl6xo4MV9/QiUWWtepR8JDEZTZo4Ix9JqjAvY1xfl2Z29RFNZKgJe7v/oKp7d2cvOnlFSmSyNVUGe3dnLx885gf7RBKFiL6l0lqQVlM/jchFJqne7KlhEOJYiIzHv70AkydLZpSTSWWKpLI0Vfh7f2sXOnlH2HBo1Y94t4KJ3zeaN3lEyUpEP3RZ/2HWIeDpr5t67nnqdnT2jxFNK0xFJpPnO1adw6Yo5dIdj3HL+Yj517kLOaq5hw74B9vZFaJ1bzv/9m3exq3uYULGXeDKTMwd5XILvX7OSOy5ZRlXQx6b2QWNS1OW7uHU2pcVeXusM80rHEKFiD8tml3LtGU1s6wxTV6YiM3/5suVcc/p87nrqdV7pCLOkPkSgSGnXVjdVcs8f9/JP5y/mWx9spSZUzB92HTJO7KlMltrSYkC1AUAkkWJL57AZo3MrA7zSMURjhZ+WWSHOX1bHnkOjrLtwKbt7RugeTlDsVY7DmnB+6dLlbOkMs7NHbVhYMruMjgEV8yeVztDWH+Xp7T3s74/iYsw03Tq3nL/7K9X3kUSGuPUueax3fjiW5u/PXUh7f4R4KsussmJqQj5S6SwjiQxpa2HmdQmCPg/RVJZUJsst5y9mV/cwCJEjfzUHOPfEWvYcGjXmd4CRTY8l193yD18hDxOaqKSU50507XggP3DZ0UxX79HXZpoxzQI5phv9Pf9cGW3q0UHHNCYyf9k1JPrcEPu2Ursvx57eUfP5pjUtuC3ardm4PS9tVrKboCD3rKCffnRVjir35bZBTplXYVixVjmO9I7m7CzRJiE9wEEJTRewcf8g7186i99s6yaezhJNZQkWuSkt9qjtrlYkWIDOcIz+kSTxdNbsEllRXm6Ze8bIg55f7CJOm5Pqy4qpLvHRFY6PC9wFikC5XCpcufYDCBV7KPa6GYylGIym+MkL+4mlstSV+sxk4XIJpIALltfz3xs7SGYkkWSGqE3NqklLRjLOPJUFDo2OLSTsxaorLSYrJb2WaeWjf9XE87sO8XrPWLp6wrnxrCZ+u6OHzqE4o/F0jsakEPIDfJX7PfzwOhUx9c5Ht7Gja4Q0WdJZycHBKHsO5drvE+ksf947AChBaPe12NsXMXUO+dwUF7npG0mSzEhTvxPrQmb3Wr65JZPXOXVlfsXchuHqVY05tnYY0zYAJqaI9ltCCH6+sUP5K7kFjRVBuocTJsCbz+OiNuQzjtaRRDon7kgkmaFz0ArWZr1j2pm3vqzYHNg4HEuZU5l3do/Q1h8xO6HsxzNUl/jM83sPjdI6t9xEKZ5dPpY/YIIX5gdhvGlNC396o8+0wfVnLTBbxB/a2GFInCanT27t4v4bV4/t+DsQJuTzcGJdyGwPvuaHG8y7r7ccR5IZ7nrqdePE3T0cByGM835XOG5CCADGcVn7hukNAnatc77Je91FS3NiPD2woQ2A7uEEJ9SWjItjE/C6c8qgTd32rcx699gfdh3ii5csM7FndP72YG92rfjqpkrjOG/fCGGH7mutoXzk02cBY5Gf9Rj64Mq5Jg1tpl6y7kmillXkzIXVDESSxnkfFEnXPk2bD4Q5u7k6RwbYzft6Y4Ge63VAyqtWNeYcZaDbu7WhzPJBVG2zq3vY+Let39tvgjLq/HRkZPt7UJbK0DUUI52Vxp9Qn/V1Yl2IW//nVTJDKphm/lll+UeXrLtoKTC2ySTgcZHKqICBdz31+rht9PmbI+xHrqxdVp+z0zNf/gKs3zfAYDRFQ7mKnj273E/7SF/HuA5mEg3O8cBkGpxCmhP9e762ZaJ7J0K+dkhrderK/NyydhFd4bhJb/WCKp7e3p2z6tnUPmQ0GVqDownHzp7RcZodnZ9ewW3YP8hAJGn+kpmsIRK1oSJmhXy80Rth/d5+uoYThHxuwrE0bxyK8Er7IM9s7zEM2y1gIJpib1+E/kiSn3/8dJpnhdjVPUxnOM43ntrJi/sGKPK4lR9J2wBfe2IH8VSWYo+bvz6pntct01PKCqe9dmkdv3u9h1RWjjM3aP+CnuE4Kxor2N+v7KqDVhl6h+MUuV0EfB6qS3zs6FKrdb04b6wK8vOPn055oIgX9/VTW1pMqNhLOiOZVaYcWM9fVseOg2FDAMoDRfi8brZ3jYwrj0bGtvrPSoinsowm0mY1rCdZbdPOSvX3ctsgWzrDRqUet60SQGkRUpnxWhiPS0yomQl43Xxu7SJ2HAwzYGk2dveMUBsqpq5MTaKJTNZsWf7FpgPs7YuQtLZjgiJ9fq/baDVqQ0XEkplx2gqARfWl1JUW88D6NhIZSaeljXELKCn20mU5Rgas9LQq3OMS1IR8DMVSOURW4/aLlxGOpmgbUH3sQpVJCkVyL2mdw4t7+4lY23zry4sJ+XP7ssjjYnvXCKsXVPLND7by2GtdhmAGvG7++WKlDr/14deIWqt/t8tFKisJFLkZjafwul28b8ksXtyvnLjbBqKqv7KSpuogdWV+vnr5SdxxyTKWN5TRFY5z//o2OofiigxmJQLJ8oZyPv2eZvpHE8bMp9tsc8cQ3eEYb/SOsrNnlIAlsNb9auvYmT0Vfj79nmazym6ZVcJtFy6hOxwjnsrSORSnfzTBly9fzifPXcg1p8/PmUtml/uZXe7nsVcPmnGRymRJpDIq6msixYdOa2Rn9zDVIR/PbO9hd+/YfPKzF9vpHk6QzIxpMLTWNpXJ8tOPrmLp7DK6wzEksL8/SkXAS01pMe0DMQYiSXOf1urOrw4ipTTaWFDOy3deupxbH36NDfsHaZlVoubBbd2KsArBhn0DOe2159Ao4bgikPF0lkgixZLZZaa968r8VJX42NwxRCqTZe2yen69uZN5VUG+/aGTOW9pHVee1sgq23ybymT5+SfO4MrTGukKx03+37yilVPmVfDJBzYpwlfsIZ7K8HeWVmvtsnpuffg1dvaMsqt7mJ+92M7PXurgnJYaNncMsWHfAE9sOchvtvfQPCvEOS01dIdj3H7xMu64ZBn3PLc3RxPeWBVk6ewyXtzXz7yqIH+7ci6b2gapK/PziXMWGi2EvYy3rF2UI4u0nHluZy+RRIZwPE1tqIhoMsM1q+dxVnNNzn1a+Ou0b37/iaZtRhIZkpksew6Nsr8/Sl2palc9xrQ8XL2gSs35HUO4XcIK0ucm6PPy4r4BM17VPWFKiseCW37zilZWL6jigfVthqTbLRerF1TxaseQaQOt1QnHFQH74R/3srN7lPnVQe68dLnyg+wcZvWCSr5w0VJml/uNBm7D/kGSGaU1/MrlJ42zlNSV+miZFeL2S5bxtQ+cxA1nNXH7F25ru/322+/Jn3vfNgRnIjOQfum6h+N0h2M0VgXHEYupCE9XOM6Gvf2sXlBlJp0rT2vkytMax5l6NncMce0ZTWoiykg27BswDW43n+kXyuMSnLe0jnue25szqV12coMZvK+0DRgV3qzSYhorA5QUexiIJJlVWsy+/qjajZPOsHpBFUGf2g4M6gRbPagTlvZEC/eQz80vNh3gud199EeSHBiMkcpKBiJJU+Y3eiMmlklJsYdHPnUW8WSGjZbfjgQ2tg0awRrwuphfHTSq6IDXhdftoizgJZ7KMhRVqk9dBolyhByIJI1g1BAoR9a7n9nN+n0DnDyvgte7RtRklpVEkxn6I0k2tQ+SzmKIyFAsRc9w4TiTDeV+E65e56H/n18VYCiWMgI94HWZIFYARZYpZ055MaFiDyPWys7nduHzuCgPekmmJWuX1dEzHGft0jp29yiSVexx4bK27JYHPDm7WeZXB3lmRw+dVpm0Knl71wiJdCZHiD21tZseaxuwJhCzyoppqi7hr5erowok6ugAv1X+IreLkN9N3DIl7O+P8uK+AXb3jlJX6qOutJjhmDLfVQW8NFYGqCtTwfdSmSytc8t5o3eUG89qYiCSpHs4wcKaIIEiNyPxtGnD/tEEiXQWj1vFzGisCtAzkmA4nqZ7OM6m9kE6rYP5MlLidgl6R5RKvKEiwOqmSv6wq5d5VUFuu3AJXeE4L+zp45BV31RWsql9kD/v6adzKG5i5KgdWG4GI0pzVOxVJEn7hVSX+IgkMxR5XHQOxUmkM1zcOseQCU3+KwJe6sr8xr+irtRHx2DUEJpN7YPs7Blrs4FoitFEmsbKgBFQmlSEfG6+deUKzltax5kLq42Zu660mDd6RxlNpFUYgFiap3f0mMWXfR5rrApy68Ov8bcrG42JRwJXnKqcysPxNIEiN/VlfrZ0DpPMKBPAx84+geUNZWbhYhegsVSWTe2DnHtiLfc8t9eYpZtnhcyi6pyWGl5pGyCVkVx/ZlNOWkUeN2uXKrPG2qVqnH/otEbueW4vS+pL2XNolHNPrOWrT+xgf3+URDpjzGM3rWlh9YKqHDPCNavnGbKj2/uW8xdz8/tayErY1D7ItWc0cddTrxOOp5FScnHrHDNfnzKvgtJir7lvubX6t5MtbeLXfaPnRE3etNmqIuDF61Zjp3s4rvosnWXYMstpGfKpcxcaUqEXtd3hmJnvu8MxvnDRUkNa1/1qqyFWu3tGSGQk57TUcPczu8aVEcYW609v72HzAWWmapkVwu12cTAcJ1DkHifjntvdx6b2Qb5y+Unmt4pAEZ2DUULF6p3W/aDL+fT2bk6dX2k0P93hGGuX1fPivn5Cfi8LakqoDCoyFLbmbT2WusMxI+e0G0T+2H16W7cZs8/tOsTmA2ES6Qw3nNXEze8/0Sxa+0aT9IwkSGUlw7EUJzWUG0KYT/zsygVNXDViqSwv7uvHZwUOtF+74447ut7WBGciHxxdaT1p3v3MLjOYNfvTHWNn6XZm+/T2HjNw7QNLX9fMWQ8c/eLoSUPbdXW5Xm4b5Olt3cYuubljiH390XH5g2Lov9h0gI6hGFJKvnP1KZzTUsMLe/qoK/NT5HEZ++oXLlrKFy5aaiYjnX5FwMvHzj5BOcZavi4hn4dZZX42HwjTPRzPcRRtri3hw6vn80bvKO9dVMv2g2FcQlAeLOLXrx7kwFAsx0fD/txNa1rY3TOCz+umIuClKuijyOOiYzBmVuJToa7URyqj/FH6I0miKWUG2tcXIWM5ZLqEigB7cmMFWSlZUl/KwaEYLleuc6naGSGoLS3mhJoS3rOolt/u6MEllAYhnZGGnM2vCtAyK0Tr3HJDTFQsFxXgLG7ZCUbiaUNuABZUB/jeh08xQcF294xw24VLefDFdqIpJXSSGYm0NFxelyCZkcaeXeRxGe3awtoSvv+RlaxZMsusTO1CTJfVLWB2hZ/+SJJIIk1XOM6eQ6PMqQgwEEnic7twCYHXrWKn+L2K4KTSSuM0r9LPsjll3HL+Ys45sZZfb+4kmZHEU1mqQj7WXbiE85bWcdnJDXz+F1uIJjPsOTTKN69otVb8gl3W+UJ1pcVmpa7PJXr65nNYOrvM+C3oCVYLav1dC+ru4TibO4YMUd9yIMyP/7Sf9sGY8TvxWPF99GS7ZHYZ6yyNSNDnpcvmZ9A3mjQar9ULqtjUPsiHVylhOhhVDpIb9vbnrPi+cvlJrFkyy0ygWvOgr197RhP9ownlGS0E27uGGYgkaZlVQkWgiKt+8AJZVADIT5yz0PinnTKvgse3dLFh34AhSY0VfhLpLCOJtBGcl53cYOYxu1ZBayaeseYhLVT0fFcRKOLZnb1Gq7ipfZBT51dyyrwKrjytkeZZISOMH1jfZjQoO3tGjZA7ZV4Fl53cQFc4zt3P7GI0kaFrWK3Y9UJO10G3YW3Ix1P/cI7RYGzuGDLjRB/HEE1mzHxoX7zd89xeo/nSdbETBLu/TCqT5dozmowG94W9/QXve3Ffv5k/NenQ8uDltkGe23UoZ07UxEnP39ee0cSTW7vMu1lZ4mPzgTDFXpf1m4evfeCkgovam9a0GM2OjnelF6yaWMWTaTqG4jnkSRMAu8zRC3Ct1Vp30dIcEpqffmNVMMdqcL/Vx5vaB9nfH2XZ7FJ+/vHTWd5QxmUnN9A8K2Tuf3p7txnXiYxk28Fh2gdUnKOWWSXcsnYRT2/vNtqcyqCPc1pq+NS5C0lmsmbhn6+N0kd4pNIZ2gdiZoGt8zx1fqXxL0tZDtb6XCtdbq0JzNdo2pUL+bI4HFcLh3xZfdgERwgh7rjjjg/fcccdl95+++1/EEI03nHHHSfefvvtndOSZjPAZATHrvWw466nXmd3b8Qw/4pAEZvaB/nceYs4b2kdgBkgeqL9xaYDVJf4eGB9W84EaCdP9oGYT2jWLqtn3SNbeGJrNxI4p6WGrnDcmMo0m/V5LMGZzhpVbfdwnCe2HOSXr3Ry/wtt3P9CG/v7oqQsx1Q9MW/YP5izOvq7s0/gya1KPQqKMfu8bhorA6xdWsc3n97FeklBBQAAHUdJREFUwXCcWaU+oskMa5fV0TkYJZ7KkpFjpqWvXLacK09rNJPrnkOj3HfDKg4MRtnSOaycj0t9hHwehqKpHDOF3+tmw95+3jgUYcA6B6VvNEk4NrZdr7EygItc51uPEGPbQ+eWU+LzmG2jQI45pNzvYVF9KV3hOIl0loNDMYZiKSLJNLFUljnWFnFQWplir5uY1b7fvKKVrz6xg5FERtVZYpwc51WpU2j390XZ2T1iNDcVAS8LZ4WMRgyU063dxFVV4mPbwbAxv0ng9zt7iVnkZna5n+G42g5cEfDyT+cvJpXJ8rUPvIub39diJpxoKsuy2aWGDNtXpn990mxDFqqCRTRUBoglMyZdLUyqAl5DzKIpFYV29YIqrj1DOb5qh9BvX3WyyefWh19jd28Ej0sFy+sejrOrZ4QrT1PnVOly6HdGT5JaKG0+EKZlVgmXtM4x5U1mstz9zC4uWdFAz3CcW9YuMmaFpbPL2Nw+yF+fNJuLW+cYQnFaUyVv9I5SE/Kxq3fUqKG/fdXJ3HnpclafUGXaSavFtWDW5fnGFa2sWTLLfK4u8XHrw68ZoqiF6dpl9WaVqAVI/gRqX6Bo9bvW5mgtTl2Zn0ta5/CFX20lns4awfBG72iORlkTF73qveX8xTl11ytVuyo+fyFmX8Tpes8u93PXU6+zvz+qdmp53Goes03wWhhrYaaF+ab2QUP28u9NpZUjbV2Z34yD/DrctKbFCDY7cfjceYvMnKTvy5+XC9VFt7fdFUB/P29pHev39vOK1fb2+Th//n56ezcXt87JWVR+8j9fZvOBsCH22vxvN9dpWVER8PKj608zhOX6MxdY7+uYdiR/Uas1O7rv7H2v6yWEYCCSzCFPUkr290eNeUtrE7U8sGt37GPDnv7scj+nzq+kOxxjIJqytJseM8/kt39XOM6rHUPGRJTKZPF53YbM63F9y1p1AKpOO+jzGtOs3dxpl0m6vD97qYPu4TjzqoIsm11qxrsmVrpf9fj/1ytX8Lcr544bX3btkt29o9BGHrsTdL7mZyKCM52zqP4d5eP3HinlYiFEBfAbKeWpkz54GNCB/mZ6VpF2bsoPMmc/J2hJfWnOgW46uF3+oYqFguBNFJpfwx7oDnLjLeiQ//pwNvtpt3bY41TAmKOxidRqMeb8g8wqAl4S1jZqGDv4z+6oq4PK/dP5i3jv4lo+cf/LvHogbAJezQr5GI6l8Fm7VwSCaDKd4/is4fO4SGeylsMguITSVvi9Lr7/kZWc3VIzrn100LD8YH6q3ip0u/3gOsg9UsB+nIHd6Tof2glOOx7rs11uWtOSE+lWO/Tmh5W3H36HEMZhtKHCTziapCxQxMBoctzOrNaGMiKJtDlHyH7AZaHxpT9rB0P7EQ/559boHWb6oEP7QZyFDtScLD+7g6yObDwV7GnqctnPCSp0+OhER3EUih69Lk/VPNO4VBMFq5zpsSP2suWfcaUDl9kP7LS377EO0jlRv052HMpEecz0/LbJ2uVo1G26z+kYXPYjK+z9qh2DW+eW88inziyY3kyCl052ztxE6RTqJ93O+r21H3I71SG2ha7psTjZ+1uoz3R5Jqv7ZO2V//x02mAmwWcLHU481Vi240gO29wkpTxZCPGKlHKF9durUsp3TVn6GUITnJlOTvkHJFoFZ51lsrJPxPZDE9fv7Tf3aQGog0YVOijNPug/cf9GDo0mqQ35mF3uN8cDRJMZE1grllI7V9xCxTiwC2/7iawtdaXjAvfZo/Paz5/Rh/K1D8RIZnIdYMEKMpbO4i/y5ASigumf6wGKBFQGi1jeoMwQ3WEVzExvpdYHTmry4hLwpUuX55yUqwmCPqYgP/IuKPIzpyIAqEBRA6NJ5lT4c4SI3gmi6945FCeezuTUpdBxDvaXRhNEUD41i2eX5vSxzqsQ6dAB/exkU/dfyOfhvhtOy9nhkB9leCJBbz+HyX5v/nlWk02uE734E01ydiI30wNKJ5rA86NTH63JbzrlsQeePJy8piPwCwnPwy3vsQoqOhUJOhrpTjetQvP30ar7sR5bb5WAskdSz+MZvPZwUeg8v5mU/0gIzgbgDOAli+jUoDQ4Kw6jHpNi5cqV8ubv/sJErtUno9oxnY63Cwu9ai0LFFFd4jMToZ60oHBUSTvTtofjB+UboiPhelyCn3/8dJO3/cBAGNvqCkz6whfSetx24VIeeqmdvkjShL3PD5l+pAh43aQy2XExR2D86boa2vfhoZfacw43rAh4WT6nLGeVHkmqw+d0u9vJaP55O3bY20qvWrRWyn5Wk/0gQ01S9TZJ+6pWkxz7ChAYp5EotFLNP8eq0G/5J2wX0vxpFFpxH+6kNNXBpXbCVqi+9jPCDndSfLOPHNE40sNhp5vG0RIYR6O8b6V8JkKh9jreZXq74S+tvY70HTsSgnM18EHgZNRZVH8D3Cal/O8Zl2IKrFy5UmYu/orRYhTq3Omo6AodfqjT0wRIawXsp0Zr+DwuGisDOSc1m1gcFvQe/FK/h3QG3rtYObeWBYrwe91GqAMm9kAslTGHG+qYGg+91E5GwtpldXz392/kBFTTQeOGbCGpC2lhPC4VF2fU8tfILyuMD7FvR0OFCvOu+V1+SHN9vtOKxnJDwuxmOX1C9JwK/zhTnNacFSJ3dpMJKFOa2+UyGpxCKviJDryE3LGhzXj5pouJtBB2E9lMTCOFiEUhc0E+pnqhZ6qBmE46ur76s72cwHGbUA93Mj8axOPNXO2+WXm9FVfwh2uSe6uU/82G0wYzw2ETHOvhRcB7UXLyt1LKHUe/iGMaHB0U6/ozm8atgO3qYq3aL6SWfXBDu9GeaE2EPfhTc02Qff1R0ll1uvDBoXgOKdCmqr68wGdFLkGRx8Wn3tPM/752kK15x9rb4XUJSorVqauvtA+NMycVOt/GDmH9o7tIAHVlxfRHkiTTWUqKXJQHfYac1JQUcWg0aUiZz+2y4h1kTNh3HcBqYDRhIoU2VPi5+8oV4075tptiYOwwSo9L0FQdZHfvaI4v01igK2HIzURaDI3887Xs986UjExXTT+R9mwm2pSp/AWOdHJ6M1Zwx8qscSTlcCZzB39p2gsHR44ZExwhROVkCUopB45S2QxWrlwpv//w0+O0LxOZJOx+D1qboF+KFV/8zf/f3rlHWVVfd/yz5z0MMwwPo6OAgoEKEkWkwTQtTeMDYmoxqyYQzVJrHk1TG1pXTbVZmhqz0iSaNNikjTbGxFgTHzE+iNFKamK0BoGIiiQ8AiogRNCBwRmYgWH3j/M7hzOXe+feGe517gzfz1p3zTm/87i/e39zztl3//be38Rz88UPvIMLZ43v0RZ7QtKquZnqr3VVFTTWVyexLH2JYclHpUXGSpxNVGFQXRF5WNIemFOPa2LEsBrmTmvhticj9dUzp7yNxc9vTeqAxH89pOHF6tCZU2ZxtcjMarONtZW8cN3crP2MDcpYPC0Ofs4Mlsv0lmVTKM9H5o0tX7BqvhtgvviUXAZNqW6w/fW2lNtDXwaJKCX6/xJ9JZeB05ua+AqiZBMDxgOtYbkZeAWYUOxObtzRnsRRxCrXEFXFjT0vdy3flHgM0lIEaemDs7/6czo6uxODJBYoG9NQQ1vQQ4m1eSrsoGJQprT93v0H2JsK1B05rIZ93Qdo79zPAT+Y2jxuZD3z/3A8N/7PmmTf6opIWfaAR+nIB4hK6rs7+w9AVWUFw1JyCwf8oIjhiUc10FBbRXtXN1ZRkRhz67ZHmjWxcTNyWDV/OvkoHnru1aTU/EPPvcqxzQdLwl+/eDWtHfu47amNSaAvRN6ZdKlsyB60GXvJMrMAMo2Btj37OH7UMMYM7+4xPXfxrUsLfmDH++X6m2u/XMQGcFpWAzhEWiNTBqTQ8/eVdIB1NsMpc3u5/nrN9zmEOBz6Kssjg0jkojctqgkAZvZfwI/d/eGw/j7g/FJ05s3O/eDO9LEj2NHelRgjaQ/OlXNOOuTBFevFxNMhsSEQxZJU0NqxL9FVOvW4Jra/2cm2XZ2MGV6TVMStqaxgRH0121PKuhXAUU211FVVUl1p/L6tM2uK8s6OLi5/79t5YOWWJJanrrrqkH3TekGdQSAuk8baSr50wak9AqEv/c4zXH3uFLbu3MO2ts4khic2fLodNrXuYcKYBt799jE8sW4H1z/0Ik311Ymq7JbWDta99iZbd+6hobYKzFi0IIoTX7RkbeLdSWtwpaPZ0229aaqkvR6ZgdP5HoqZN7ZcN7pCb4ALz5qcGMqLlhzUNHlkVaS8+8iqrT2C2NM3ylI8uPMZTn0xrAYy26NUBuBQRA/f0iODW+SiNw9OzBnu/vF4xd1/amZfKUVnhtdWcc15J3P94tVJ3Et6quOqc6ew4uVW7l72CsOqK2nt2Mf1i1fTVFeVPLAWnjWZDdvfZPPOvVF8S/CKdOzrpraqgg2vdyRVarelyv13dR/guOY6prQ0MrWlKREBjA2gOK4FoLICaisrOefko/nF2u1cOeckVrzcSkNNJZPeNpyG2qqknsqO9i5ebd3DASKDaXRjDe17u5OA3Hh6J1tcSfveyIO1u3M/j6zaypcuOJVFS9Zy9snHJAJqC8/qKQ4YEwtsxiJpcVD1trZOdndGBuCiJWuBg4ZH5rkK8SjExkbm9Ep8vvTfzOVS3/xPP37kIbWOcvULSn+jzGeY9eWXa6n6Wsh5+/oL+0hGD9/SI4Nb5KKQLKpHgV8Cd4Smi4DZ7j6n2J2ZesppPvkT/94jsPX86cfyRnvXIVMicezM2OZIWTptHHz4lqd5esMb1FdXcMaE0fxi3fYkdqbSYER9FCyb/uTpQntpNW+IpqDqqit7ZFqlDa90QHO2uI1Cam1ke9jHHpzMWiuFxIbkqpcSFzw8ZkR93oJlpTZAyiGYcLDEvWRSLvU6yo1y63+59ac3lOkkBiuHkyY+CvgcMDs0PQFcV4og49EnTPHGBTf2aIuDaOOMnNgrsqV1T1KNtmNfN7MnjeHTZ05i4Q+fZeuuvYcEA9dWVXBscz2VBuu3tzO2uY5tbZ3JueMKyDHpon3xe1/34Cq6up2aqgo69x9I0s7n3/x0Euh711+/q6AqmZnFjAopjlVo8afebj7lYFTElMNNspy+D3H4aDz7T1++O33PopzoT5AxkGRLLTSzxmjV38x3TH85uqmOyWNHHFIzJq6am66Zkuoh08c1M7Wlifk3P52kXc8Y18w3PzKDT9/5LMtebqW6IjKWtoSpr6279vLxP5mYxLLAwXo1cZBsOgPpkVVbk0KA40fWs6O9i7nTWli0ZG1i3Hx+3rRD6q2kvTZpV2qm6zqbmzU9/XP+N57MWacl81y9ucXLyZ2bnup4q6rfZlLo91EOxpjITzn9fw82+vLd6XsWg4FCPDjvAG4H4rTxHcAl7r6q2J3JlGqIM3biaZWubu8h4hhrCjXWRnZaPK1UX2Vcc960rOnLaUYOq+bZa88pqG/pDCMgCaiNz99bRdt0Nd3+TIekg3V7mwIrxIOT7XMNxBRNpgGYLUgZyueXYrn0QwghRE/67cEBbgaucPfHw4neA9xCJN9QVDq6ujn/G0/S3nWwMF2cLRV7btJVcwHWBfHFSUc10LFjfySfUFnJ3cs3JenmmQKPY0fW0965P5FTyCT98E0H/8aVkzNlIdIZOemCd9PHNSfF6+ZOa0kqx0LfMmXmTmvJGkgcU2j2UTbS3h7In+lULNKZcLGGWGwEpimXX4rl0g8hhBCFUYiB0xAbNwDu/nMzayhFZ156vZ2OkKacKTqYLcvozqWv9Kh63EOt2z3xnjyyaivfvWxWTmG+TDIfvhCVtY+DitMGRLaaLfFxTXVVXDhrPBfOGs/Fty5NavVkm6LqrR9AQcrP/SFfplOpiN8jn6pxuWTslEs/hBBCFEYhU1Q/Bn4NfD80fQQ43d0/UOzO1LZM8hM/dhOTj2lKCsWlp0/SJf3TxkxaSyet0Jw+vi8l99Oek9ue3MCGHe10e/bpoWxkE2fszzTS4cR9lDpmRDEpQgghyoHDmaK6DLgOuC+sPxHaSsLkY5q4/2/f3UO0MCYuQvfy6+1J+7DqSrbuiuQOGmuj4npNdVWJEZGPbJ6U9K/1uHBg7HkphGyF5IrlASjUsCh1/Q3V9xBCCFHOFJJF1Qp8GsDMKommrHIrTB4G9dWVwMGHeGvHPhprK2nbu58PzRyXxKF8aOa4JKamc393Up8mLSMABx/CbXv29ZAXSNOX6rKFeirynTPTeCtkiiqe1krrSPVmWJQ6ZkQxKUIIIcqZQqao7gQ+CXQDy4AmYJG731DszsR1cNLxN3EF3rjYXXoaKTYS0tNSsecmnfINZM3QGSjijJx8gpSZwczpzDJNDQkhhBCHV+hvpbtPN7OLgBnAVcAKdz+l2J2cOOUUH/HhG3vErpz9tV8kHppMAyVXXEvay5GtqN5A05/4FcW8CCGEEIeSy8CpKODYajOrJhLYfNDd9wG9W0X9ZNeefUnsSkxDTTRt1Vhbech0SDqu5eJbl3L94tXRlE4Q7Jz0tuG0Bd2p2z86q2wMg7jfvXluLr51aY8YonzHDEayfU4hhBCiGBRi4NwMvAQ0AE+Y2fFAQTE4ZjbXzNaY2Xozuyrf/kc31R1SC+Wa805m9qQxfPeyWYmXJvOhmMSqeKQFdc15J3P/5X9My4g6Vm7amYhKDhbizzPY+t1XjpTPKYQQ4q2nkCDjm4CbUk0vm9mf5TsuBCR/Ezgb2AwsM7MH3X11rmN+37Y3a7p2ui1b9k6uQOC3IhC2FFNHR0oA75HyOYUQQrz15IzBMbOPuPsdZnZFtu3u/rVeT2z2LuBfYtVxM7s6HPevuY6pbZnk879wR49pp3Rgba6CfwOJSvgLIYQQA0d/6uDE1Yob+/mexwGbUuubgUMsADP7BPAJgIZRx7DwrMmJV2RqSxMvbNlFe1c3Kzfv4tlXWtnd2c30sSPKwrgBeSGEEEKIciSngePuN4e/15WyA+5+C5G2FaNPmOJwqFTCmIYaRg6rZnhtFbs79ySCl+WASvgLIYQQ5UfeIGMzm2hmD5nZdjN7zcweMLOJBZx7CzAutT42tOXkzc79fOx7y5g7rYXZk8Zw5ZyTmD1pDA11kabU6IaaKIg4yDAIIYQQQmSjEKmGO4mChWPtqQXAD8gy3ZTBMmCSmU0gMmwWABf2dkBlhSVp4rFX5MJZ44+4GjBH2ucVQgghik0hBs4wd/9+av0OM7sy30Huvt/MLgceBSqB77j7i70dc8LoBmZmpInDkTcNJJ0nIYQQ4vAoxMD5aahh80OiAn/zgYfNbBSAu7+R60B3fxh4uNDODKup1AMdBS4LIYQQh0shUg0be9ns7l5IPE5BzJw505cvX16s0wkhhBBiiNOfNHEA3H1CabokhBBCCFEaeiv09xl3/0pY/qC735Pa9kV3/+eid8ZsN7Cm2OcVRWUMsGOgOyF6RWM0ONA4lT8ao8HB8e5+VGZjbwbOr919RuZytvViYWbLs7mZRPmgMSp/NEaDA41T+aMxGtz0VgfHcixnWxdCCCGEKBt6M3A8x3K2dSGEEEKIsqG3IONTzayNyFtTH5YJ63Ul6s8tJTqvKB4ao/JHYzQ40DiVPxqjQUzeNHEhhBBCiMFGXi0qIYQQQojBhgwcIYQQQgw5ysLAMbO5ZrbGzNYHWQhRQsxsnJk9bmarzexFM1sY2keZ2WNmti78HRnazcxuCuPzvJmlSwZcEvZfZ2aXpNpPN7MXwjE3mZky7/qBmVWa2bNmtjisTzCzpeF7vcvMakJ7bVhfH7afkDrH1aF9jZnNSbXruisCZtZsZvea2W/N7Ddm9i5dS+WFmf1DuNetMrMfmFmdrqUjAHcf0BeREOfvgIlADfAcMHWg+zWUX0ALMCMsNwJrganAV4CrQvtVwJfD8rnAT4kCzM8Alob2UcCG8HdkWB4Ztj0T9rVw7PsG+nMPxhdwBXAnsDis3w0sCMvfAv4mLH8K+FZYXgDcFZanhmuqFpgQrrVKXXdFHaPvAR8LyzVAs66l8nkBxwEbgfqwfjdwqa6lof8qBw/OO4H17r7B3buIRD3nDXCfhjTuvtXdfx2WdwO/IboJzCO6WRP+nh+W5wG3e8SvgGYzawHmAI+5+xvu3go8BswN25rc/Vce3RluT51LFIiZjQXeD3w7rBvwXuDesEvmGMVjdy9wZth/HvBDd+90943AeqJrTtddETCzEcBs4FYAd+9y953oWio3qoiygauAYcBWdC0NecrBwDkO2JRa3xzaxFtAcL+eBiwFjnb3rWHTNuDosJxrjHpr35ylXfSNrwOfAQ6E9dHATnffH9bT32syFmH7rrB/X8dO9I0JwHbgtjCV+G0za0DXUtng7luAG4FXiAybXcAKdC0NecrBwBEDhJkNB34E/L27t6W3hV+LqiEwQJjZnwOvufuKge6L6JUqYAbwn+5+GtBONCWVoGtpYAnxT/OIjNFjgQZg7oB2SrwllIOBswUYl1ofG9pECTGzaiLj5r/d/b7Q/PvgEif8fS205xqj3trHZmkXhfNu4C/M7CUil/d7gUVEUxpxgc7095qMRdg+Anidvo+d6Bubgc3uvjSs30tk8OhaKh/OAja6+3Z33wfcR3R96Voa4pSDgbMMmBQi2muIgroeHOA+DWnCfPKtwG/c/WupTQ8CcfbGJcADqfaLQwbIGcCu4H5/FDjHzEaGX0nnAI+GbW1mdkZ4r4tT5xIF4O5Xu/tYdz+B6Jr4X3e/CHgcuCDsljlG8dhdEPb30L4gZIZMACYRBa3quisC7r4N2GRmfxCazgRWo2upnHgFOMPMhoXvMB4jXUtDnYGOco7+bziXKJPnd8BnB7o/Q/0F/DGRy/x5YGV4nUs0z/wzYB2wBBgV9jfgm2F8XgBmps51GVGw3Xrgr1LtM4FV4ZhvEKpm69Wv8XoPB7OoJhLdVNcD9wC1ob0urK8P2yemjv9sGIc1pDJwdN0VbXymA8vD9XQ/URaUrqUyegHXAb8N3+P3iTKhdC0N8ZekGoQQQggx5CiHKSohhBBCiKIiA0cIIYQQQw4ZOEIIIYQYcsjAEUIIIcSQQwaOEEIIIYYcMnCEEIdNUNT+VFg+1szuzXfMYbzXdDM7t1TnF0IMDWTgCCGKQTORCjPu/qq7X5Bn/8NhOlHdESGEyIkMHCFEMfgScKKZrTSze8xsFYCZXWpm95vZY2b2kpldbmZXBGHKX5nZqLDfiWb2iJmtMLNfmtlJof2DZrbKzJ4zsydCpdjPA/PDe803swYz+46ZPRPOOy/13g+Y2c/NbJ2ZfS60N5jZT8I5V5nZ/AH5xoQQJaUq/y5CCJGXq4Bp7j49KNQvTm2bRqRYX0dUHfaf3P00M/s3IumBrwO3AJ9093VmNgv4DyL9rWuBOe6+xcya3b3LzK4lqgB8OYCZfZGonP5lZtYMPGNmS8J7vzO8fwewzMx+AhwPvOru7w/HjyjVlyKEGDhk4AghSs3j7r4b2G1mu4CHQvsLwClB1f6PgHsiqSAgKqUP8BTwXTO7m0gkMRvnEAmT/mNYrwPGh+XH3P11ADO7j0im5GHgq2b2ZSIJjF8W40MKIcoLGThCiFLTmVo+kFo/QHQPqgB2uvv0zAPd/ZPBo/N+YIWZnZ7l/Ab8pbuv6dEYHZepRePuvtbMZhDF8XzBzH7m7p/vzwcTQpQvisERQhSD3UBjfw509zZgo5l9ECK1ezM7NSyf6O5L3f1aYDswLst7PQr8XVCKxsxOS20728xGmVk9cD7wlJkdC3S4+x3ADcCM/vRbCFHeyMARQhw2YRroqRBcfEM/TnER8FEzew54EZgX2m8wsxfCef8PeA54HJgaBxkD1wPVwPNm9mJYj3kG+BGR0veP3H058A6iOJ2VwOeAL/Sjv0KIMkdq4kKIIYmZXUoqGFkIcWQhD44QQgghhhzy4AghhBBiyCEPjhBCCCGGHDJwhBBCCDHkkIEjhBBCiCGHDBwhhBBCDDlk4AghhBBiyPH/lNrvpAsRygAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helper function from the ploting library\n",
    "results_plotter.plot_results([log_dir], 1e5, results_plotter.X_TIMESTEPS, \"PPO 5x5 Random Road Env Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "jlIjD1v9NtYR",
    "outputId": "5b6fa251-f562-41b4-cab6-cbb6d3a3f6ad"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reward function value curve\n",
    "plot_results(log_dir, \"PPO 5x5 Random Road Env Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JbCFt4cohS1A"
   },
   "outputs": [],
   "source": [
    "# outputting the logged reward values\n",
    "!cat /tmp/gym/monitor.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wkMIZobSSWBv"
   },
   "outputs": [],
   "source": [
    "env_random = GreedyGovernanceKernelWrapper()\n",
    "# logs will be saved in log_dir/monitor.csv\n",
    "env_random = Monitor(env_random, log_dir)\n",
    "# training sample environment policy for checking the env functionality\n",
    "callback = SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir)\n",
    "\n",
    "ppo_model_5x5_greedy_random = PPO(\"MlpPolicy\", env_random, verbose=0)\n",
    "ppo_model_5x5_greedy_random.learn(total_timesteps=120000, callback=callback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "id": "5479k3ype49X",
    "outputId": "285f4f32-21bf-4d32-be0f-ed2e470dec74"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helper function from the ploting library\n",
    "results_plotter.plot_results([log_dir], 1e5, results_plotter.X_TIMESTEPS, \"PPO 5x5 Random Road Env Greedy Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "foR8syoSSXel",
    "outputId": "002e0af0-8a0f-49bf-97ef-b689852cb513"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reward function value curve\n",
    "plot_results(log_dir, \"PPO 5x5 Random Road Env Greedy Governance Results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wUQDE24De9Un"
   },
   "outputs": [],
   "source": [
    "# outputting the logged reward values\n",
    "!cat /tmp/gym/monitor.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}