notebooks/graph_experiments_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph Environment Experiments (2)\n",
    "\n",
    "Referenced Work: [Momennejad et al., 2017](https://www.nature.com/articles/s41562-017-0180-8)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment below lines and run this cell to install neuronav and requirements for colab.\n",
    "\n",
    "#!git clone https://github.com/awjuliani/neuro-nav\n",
    "#!pip install ./neuro-nav[experiments_remote]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuronav.envs.graph_env import GraphEnv, GraphObservation\n",
    "from neuronav.envs.graph_templates import GraphTemplate\n",
    "from neuronav.agents.td_agents import TDSR\n",
    "from neuronav.agents.dyna_agents import DynaSR\n",
    "from neuronav.agents.mb_agents import MBV, SRMB\n",
    "from neuronav.utils import run_episode\n",
    "\n",
    "import numpy as np\n",
    "from neuronav.notebook_helpers import (\n",
    "    plot_graph_experiment_results,\n",
    "    get_scores,\n",
    "    AgentParams,\n",
    "    init_agents,\n",
    "    calc_revaluation,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set parameters for experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_eps = 100\n",
    "num_steps = 3\n",
    "num_reps = 10\n",
    "\n",
    "agent_dict = {\"TDSR\": TDSR, \"DynaSR\": DynaSR, \"SRMB\": SRMB, \"MBV\": MBV}\n",
    "\n",
    "agent_params = AgentParams(\n",
    "    poltype=\"softmax\",\n",
    "    beta=1e-4,\n",
    "    epsilon=0.1,\n",
    "    learning_rate=1e-1,\n",
    "    gamma=0.9,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define experiment-specific functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_init_prefs(env, agents, num_eps, num_steps):\n",
    "    init_state_prefs = []\n",
    "    for agent in agents:\n",
    "        for i in range(num_eps):\n",
    "            agent, _, _ = run_episode(env, agent, num_steps, 0)\n",
    "        Qs = agent.Q[:, 0]\n",
    "        max_Q_idx = np.argmax(Qs)\n",
    "        init_state_prefs.append((agent.__class__.__name__, max_Q_idx, Qs))\n",
    "    return init_state_prefs\n",
    "\n",
    "\n",
    "def control_func(agent_types):\n",
    "    env = GraphEnv(GraphTemplate.human_b, obs_type=GraphObservation.index)\n",
    "    agents = init_agents(env, agent_types, agent_params)\n",
    "    env.reset()\n",
    "    env.render()\n",
    "    init_state_prefs = gen_init_prefs(env, agents, num_eps, num_steps)\n",
    "    control_state_prefs = gen_init_prefs(env, agents, num_eps, num_steps)\n",
    "    return calc_revaluation(init_state_prefs, control_state_prefs)\n",
    "\n",
    "\n",
    "def reward_func(agent_types):\n",
    "    env = GraphEnv(GraphTemplate.human_b, obs_type=GraphObservation.index)\n",
    "    agents = init_agents(env, agent_types, agent_params)\n",
    "    init_state_prefs = gen_init_prefs(env, agents, num_eps, num_steps)\n",
    "\n",
    "    env.template_objects[\"rewards\"][3] = 45\n",
    "    env.template_objects[\"rewards\"][4] = 0\n",
    "    env.template_objects[\"rewards\"][5] = 30\n",
    "    env.reset()\n",
    "    env.render()\n",
    "\n",
    "    rew_state_prefs = []\n",
    "    for agent in agents:\n",
    "        for i in range(num_eps):\n",
    "            start_state = np.random.randint(3, 6)\n",
    "            agent, _, _ = run_episode(env, agent, num_steps, start_state)\n",
    "        Qs = agent.Q[:, 0]\n",
    "        max_Q_idx = np.argmax(Qs)\n",
    "        rew_state_prefs.append((agent.__class__.__name__, max_Q_idx, Qs))\n",
    "    return calc_revaluation(init_state_prefs, rew_state_prefs)\n",
    "\n",
    "\n",
    "def transition_func(agent_types):\n",
    "    env = GraphEnv(GraphTemplate.human_b, obs_type=GraphObservation.index)\n",
    "    agents = init_agents(env, agent_types, agent_params)\n",
    "    init_state_prefs = gen_init_prefs(env, agents, num_eps, num_steps)\n",
    "\n",
    "    env.edges[1] = [4, 5]\n",
    "    env.edges[2] = [3, 4]\n",
    "\n",
    "    env.reset()\n",
    "    env.render()\n",
    "\n",
    "    trans_state_prefs = []\n",
    "    for agent in agents:\n",
    "        for i in range(num_eps):\n",
    "            start_state = np.random.randint(1, 3)\n",
    "            agent, _, _ = run_episode(env, agent, num_steps, start_state)\n",
    "        Qs = agent.Q[:, 0]\n",
    "        max_Q_idx = np.argmax(Qs)\n",
    "        trans_state_prefs.append((agent.__class__.__name__, max_Q_idx, Qs))\n",
    "\n",
    "    return calc_revaluation(init_state_prefs, trans_state_prefs)\n",
    "\n",
    "\n",
    "def policy_func(agent_types):\n",
    "    env = GraphEnv(GraphTemplate.human_b, obs_type=GraphObservation.index)\n",
    "    env.template_objects[\"rewards\"][3] = 0\n",
    "    env.template_objects[\"rewards\"][4] = 15\n",
    "    env.template_objects[\"rewards\"][5] = 30\n",
    "\n",
    "    agents = init_agents(env, agent_types, agent_params)\n",
    "    init_state_prefs = gen_init_prefs(env, agents, num_eps, num_steps)\n",
    "\n",
    "    env.template_objects[\"rewards\"][3] = 45\n",
    "    env.template_objects[\"rewards\"][4] = 15\n",
    "    env.template_objects[\"rewards\"][5] = 30\n",
    "\n",
    "    env.reset()\n",
    "    env.render()\n",
    "\n",
    "    policy_state_prefs = []\n",
    "    for agent in agents:\n",
    "        for i in range(num_eps):\n",
    "            start_state = np.random.randint(3, 6)\n",
    "            agent, _, _ = run_episode(env, agent, num_steps, start_state)\n",
    "        Qs = agent.Q[:, 0]\n",
    "        max_Q_idx = np.argmax(Qs)\n",
    "        policy_state_prefs.append((agent.__class__.__name__, max_Q_idx, Qs))\n",
    "    return calc_revaluation(init_state_prefs, policy_state_prefs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run experimental conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Control Condition\n",
    "all_cs = get_scores(control_func, agent_dict, num_reps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reward revaluation condition\n",
    "all_rs = get_scores(reward_func, agent_dict, num_reps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Transition revaluation condition\n",
    "all_ts = get_scores(transition_func, agent_dict, num_reps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Policy revaluation condition\n",
    "all_ps = get_scores(policy_func, agent_dict, num_reps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 7000x700 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_dict = {\n",
    "    \"Control\": all_cs,\n",
    "    \"Reward\": all_rs,\n",
    "    \"Transition\": all_ts,\n",
    "    \"Policy\": all_ps,\n",
    "}\n",
    "plot_graph_experiment_results(list(agent_dict.keys()), result_dict, \"experiment_2\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "d4416b23800dac0499c77199e6a61cce292c2d7a147d61370d8d704e5424b5c2"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}