From 31aee7703a3ca62df7eb890ba052c83ca45c3247 Mon Sep 17 00:00:00 2001 From: tobirohrer Date: Fri, 3 Nov 2023 14:05:32 +0100 Subject: [PATCH 1/4] refactored rl solution --- example_solutions/helper.py | 19 + example_solutions/observation_wrapper.py | 31 + example_solutions/optimal_control_problem.py | 0 ...ement_learning_sample_implementation.ipynb | 1453 +++++++++++++++++ 4 files changed, 1503 insertions(+) create mode 100644 example_solutions/helper.py create mode 100644 example_solutions/observation_wrapper.py create mode 100644 example_solutions/optimal_control_problem.py create mode 100644 example_solutions/reinforcement_learning_sample_implementation.ipynb diff --git a/example_solutions/helper.py b/example_solutions/helper.py new file mode 100644 index 0000000..21e20df --- /dev/null +++ b/example_solutions/helper.py @@ -0,0 +1,19 @@ +import pandas as pd +import numpy as np +from typing import Tuple + +# Start and end Index of data used for testing +TEST_INDEX_START = 4380 +TEST_INDEX_END = 8500 + + +def read_data() -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + load = pd.read_csv('../building_energy_storage_simulation/data/preprocessed/electricity_load_profile.csv')[ + 'Load [kWh]'] + price = pd.read_csv('../building_energy_storage_simulation/data/preprocessed/electricity_price_profile.csv')[ + 'Day Ahead Auction'] + generation = pd.read_csv('../building_energy_storage_simulation/data/preprocessed/solar_generation_profile.csv')[ + 'Generation [kWh]'] + return np.array(load), np.array(price), np.array(generation) + + diff --git a/example_solutions/observation_wrapper.py b/example_solutions/observation_wrapper.py new file mode 100644 index 0000000..382ecbd --- /dev/null +++ b/example_solutions/observation_wrapper.py @@ -0,0 +1,31 @@ +import gymnasium +import numpy as np + + +class ObservationWrapper(gymnasium.Wrapper): + def __init__(self, env, forecast_length): + super().__init__(env) + + self.forecast_length = forecast_length + original_observation_space_length = self.observation_space.shape[0] + self.observation_space = gymnasium.spaces.Box(shape=(original_observation_space_length - forecast_length,), + low=-np.inf, + high=np.inf, dtype=np.float32) + + def reset(self, seed: int = 42, options=None): + obs, info = self.env.reset() + return self.convert_observation(obs), info + + def step(self, action): + obs, reward, done, trunc, info = self.env.step(action) + return self.convert_observation(obs), reward, done, trunc, info + + def convert_observation(self, obs): + load_forecast = obs[1: self.forecast_length + 1] + generation_forecast = obs[self.forecast_length + 1: 2 * self.forecast_length + 1] + price_forecast = obs[2 * self.forecast_length + 1: 3 * self.forecast_length + 1] + soc = obs[0] + return np.concatenate(([soc], + load_forecast - generation_forecast, + price_forecast), + axis=0) diff --git a/example_solutions/optimal_control_problem.py b/example_solutions/optimal_control_problem.py new file mode 100644 index 0000000..e69de29 diff --git a/example_solutions/reinforcement_learning_sample_implementation.ipynb b/example_solutions/reinforcement_learning_sample_implementation.ipynb new file mode 100644 index 0000000..d301724 --- /dev/null +++ b/example_solutions/reinforcement_learning_sample_implementation.ipynb @@ -0,0 +1,1453 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import gymnasium\n", + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from stable_baselines3 import PPO, SAC\n", + "from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize\n", + "from stable_baselines3.common.monitor import Monitor\n", + "\n", + "from building_energy_storage_simulation import BuildingSimulation, Environment\n", + "\n", + "from observation_wrapper import ObservationWrapper\n", + "from helper import read_data, TEST_INDEX_START, TEST_INDEX_END" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Applying Reiforcement Learning Using Stable Baselines 3\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 9.89 9.08 8.22 8.57 8.93 9.2 10.71\n", + " 3.98309 5.005 4.133 4.322 4.546 3.767 3.97 4.059\n", + " 4.326 ]\n" + ] + } + ], + "source": [ + "NUM_FORECAST_STEPS = 8\n", + "RESULT_PATH = 'rl_example/'\n", + "\n", + "os.makedirs(RESULT_PATH, exist_ok=True)\n", + "\n", + "load, price, generation = read_data()\n", + "load_train = load[:TEST_INDEX_START]\n", + "price_train = price[:TEST_INDEX_START]\n", + "generation_train = generation[:TEST_INDEX_START]\n", + "\n", + "# Create Training Environment\n", + "sim = BuildingSimulation(electricity_load_profile=load_train,\n", + " solar_generation_profile=generation_train,\n", + " electricity_price=price_train,\n", + " max_battery_charge_per_timestep=100,\n", + " battery_capacity=400)\n", + "\n", + "env = Environment(sim, num_forecasting_steps=NUM_FORECAST_STEPS, max_timesteps=len(load_train)-NUM_FORECAST_STEPS)\n", + "# ObservationWrapper combines forecast of load and generation to one residual load forecast\n", + "env = ObservationWrapper(env, NUM_FORECAST_STEPS)\n", + "initial_obs, info = env.reset()\n", + "print(initial_obs)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Wrap with Monitor() so a log of the training is saved \n", + "env = Monitor(env, filename=RESULT_PATH)\n", + "# Warp with DummyVecEnc() so the observations and reward can be normalized using VecNormalize()\n", + "env = DummyVecEnv([lambda: env])\n", + "env = VecNormalize(env, norm_obs=True, norm_reward=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using cpu device\n", + "-----------------------------\n", + "| time/ | |\n", + "| fps | 1846 |\n", + "| iterations | 1 |\n", + "| time_elapsed | 1 |\n", + "| total_timesteps | 2048 |\n", + "-----------------------------\n", + "------------------------------------------\n", + "| time/ | |\n", + "| fps | 1387 |\n", + "| iterations | 2 |\n", + "| time_elapsed | 2 |\n", + "| total_timesteps | 4096 |\n", + "| train/ | |\n", + "| approx_kl | 0.0036777142 |\n", + "| clip_fraction | 0.0229 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.42 |\n", + "| explained_variance | -0.708 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.000247 |\n", + "| n_updates | 10 |\n", + "| policy_gradient_loss | -0.00348 |\n", + "| std | 0.995 |\n", + "| value_loss | 0.234 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -5.1e+06 |\n", + "| time/ | |\n", + "| fps | 1261 |\n", + "| iterations | 3 |\n", + "| time_elapsed | 4 |\n", + "| total_timesteps | 6144 |\n", + "| train/ | |\n", + "| approx_kl | 0.0045435634 |\n", + "| clip_fraction | 0.0198 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.4 |\n", + "| explained_variance | 0.323 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.00283 |\n", + "| n_updates | 20 |\n", + "| policy_gradient_loss | -0.00426 |\n", + "| std | 0.97 |\n", + "| value_loss | 0.0201 |\n", + "------------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -5.1e+06 |\n", + "| time/ | |\n", + "| fps | 1205 |\n", + "| iterations | 4 |\n", + "| time_elapsed | 6 |\n", + "| total_timesteps | 8192 |\n", + "| train/ | |\n", + "| approx_kl | 0.004573004 |\n", + "| clip_fraction | 0.0329 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.4 |\n", + "| explained_variance | 0.611 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.00737 |\n", + "| n_updates | 30 |\n", + "| policy_gradient_loss | -0.00455 |\n", + "| std | 0.984 |\n", + "| value_loss | 0.0243 |\n", + "-----------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -5e+06 |\n", + "| time/ | |\n", + "| fps | 1169 |\n", + "| iterations | 5 |\n", + "| time_elapsed | 8 |\n", + "| total_timesteps | 10240 |\n", + "| train/ | |\n", + "| approx_kl | 0.0037469426 |\n", + "| clip_fraction | 0.0484 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.41 |\n", + "| explained_variance | 0.57 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0261 |\n", + "| n_updates | 40 |\n", + "| policy_gradient_loss | -0.00724 |\n", + "| std | 0.99 |\n", + "| value_loss | 0.00943 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -5e+06 |\n", + "| time/ | |\n", + "| fps | 1148 |\n", + "| iterations | 6 |\n", + "| time_elapsed | 10 |\n", + "| total_timesteps | 12288 |\n", + "| train/ | |\n", + "| approx_kl | 0.0058725784 |\n", + "| clip_fraction | 0.0702 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.42 |\n", + "| explained_variance | 0.751 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0108 |\n", + "| n_updates | 50 |\n", + "| policy_gradient_loss | -0.00806 |\n", + "| std | 1 |\n", + "| value_loss | 0.0147 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.93e+06 |\n", + "| time/ | |\n", + "| fps | 1139 |\n", + "| iterations | 7 |\n", + "| time_elapsed | 12 |\n", + "| total_timesteps | 14336 |\n", + "| train/ | |\n", + "| approx_kl | 0.0058474382 |\n", + "| clip_fraction | 0.0539 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.42 |\n", + "| explained_variance | 0.635 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.00986 |\n", + "| n_updates | 60 |\n", + "| policy_gradient_loss | -0.00606 |\n", + "| std | 0.999 |\n", + "| value_loss | 0.00742 |\n", + "------------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.93e+06 |\n", + "| time/ | |\n", + "| fps | 1132 |\n", + "| iterations | 8 |\n", + "| time_elapsed | 14 |\n", + "| total_timesteps | 16384 |\n", + "| train/ | |\n", + "| approx_kl | 0.003978129 |\n", + "| clip_fraction | 0.025 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.42 |\n", + "| explained_variance | 0.82 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0199 |\n", + "| n_updates | 70 |\n", + "| policy_gradient_loss | -0.00461 |\n", + "| std | 1 |\n", + "| value_loss | 0.00987 |\n", + "-----------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.89e+06 |\n", + "| time/ | |\n", + "| fps | 1127 |\n", + "| iterations | 9 |\n", + "| time_elapsed | 16 |\n", + "| total_timesteps | 18432 |\n", + "| train/ | |\n", + "| approx_kl | 0.0047321245 |\n", + "| clip_fraction | 0.0652 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.4 |\n", + "| explained_variance | 0.685 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0133 |\n", + "| n_updates | 80 |\n", + "| policy_gradient_loss | -0.00815 |\n", + "| std | 0.97 |\n", + "| value_loss | 0.00682 |\n", + "------------------------------------------\n", + "----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.89e+06 |\n", + "| time/ | |\n", + "| fps | 1125 |\n", + "| iterations | 10 |\n", + "| time_elapsed | 18 |\n", + "| total_timesteps | 20480 |\n", + "| train/ | |\n", + "| approx_kl | 0.00393809 |\n", + "| clip_fraction | 0.0249 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.38 |\n", + "| explained_variance | 0.846 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0126 |\n", + "| n_updates | 90 |\n", + "| policy_gradient_loss | -0.00404 |\n", + "| std | 0.955 |\n", + "| value_loss | 0.00849 |\n", + "----------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.85e+06 |\n", + "| time/ | |\n", + "| fps | 1122 |\n", + "| iterations | 11 |\n", + "| time_elapsed | 20 |\n", + "| total_timesteps | 22528 |\n", + "| train/ | |\n", + "| approx_kl | 0.0056245844 |\n", + "| clip_fraction | 0.0404 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.37 |\n", + "| explained_variance | 0.752 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.00556 |\n", + "| n_updates | 100 |\n", + "| policy_gradient_loss | -0.00522 |\n", + "| std | 0.941 |\n", + "| value_loss | 0.00499 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.85e+06 |\n", + "| time/ | |\n", + "| fps | 1119 |\n", + "| iterations | 12 |\n", + "| time_elapsed | 21 |\n", + "| total_timesteps | 24576 |\n", + "| train/ | |\n", + "| approx_kl | 0.0032175046 |\n", + "| clip_fraction | 0.0312 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.35 |\n", + "| explained_variance | 0.827 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.00478 |\n", + "| n_updates | 110 |\n", + "| policy_gradient_loss | -0.00691 |\n", + "| std | 0.935 |\n", + "| value_loss | 0.00943 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.83e+06 |\n", + "| time/ | |\n", + "| fps | 1110 |\n", + "| iterations | 13 |\n", + "| time_elapsed | 23 |\n", + "| total_timesteps | 26624 |\n", + "| train/ | |\n", + "| approx_kl | 0.0033950265 |\n", + "| clip_fraction | 0.0379 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.35 |\n", + "| explained_variance | 0.738 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.00281 |\n", + "| n_updates | 120 |\n", + "| policy_gradient_loss | -0.00417 |\n", + "| std | 0.928 |\n", + "| value_loss | 0.00453 |\n", + "------------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.83e+06 |\n", + "| time/ | |\n", + "| fps | 1054 |\n", + "| iterations | 14 |\n", + "| time_elapsed | 27 |\n", + "| total_timesteps | 28672 |\n", + "| train/ | |\n", + "| approx_kl | 0.005857446 |\n", + "| clip_fraction | 0.0508 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.33 |\n", + "| explained_variance | 0.841 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.00559 |\n", + "| n_updates | 130 |\n", + "| policy_gradient_loss | -0.00754 |\n", + "| std | 0.905 |\n", + "| value_loss | 0.00871 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.79e+06 |\n", + "| time/ | |\n", + "| fps | 1010 |\n", + "| iterations | 15 |\n", + "| time_elapsed | 30 |\n", + "| total_timesteps | 30720 |\n", + "| train/ | |\n", + "| approx_kl | 0.005098461 |\n", + "| clip_fraction | 0.0383 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.31 |\n", + "| explained_variance | 0.684 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0102 |\n", + "| n_updates | 140 |\n", + "| policy_gradient_loss | -0.00649 |\n", + "| std | 0.895 |\n", + "| value_loss | 0.00368 |\n", + "-----------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.79e+06 |\n", + "| time/ | |\n", + "| fps | 996 |\n", + "| iterations | 16 |\n", + "| time_elapsed | 32 |\n", + "| total_timesteps | 32768 |\n", + "| train/ | |\n", + "| approx_kl | 0.0066045905 |\n", + "| clip_fraction | 0.0508 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.3 |\n", + "| explained_variance | 0.862 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.01 |\n", + "| n_updates | 150 |\n", + "| policy_gradient_loss | -0.00624 |\n", + "| std | 0.885 |\n", + "| value_loss | 0.00755 |\n", + "------------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.79e+06 |\n", + "| time/ | |\n", + "| fps | 999 |\n", + "| iterations | 17 |\n", + "| time_elapsed | 34 |\n", + "| total_timesteps | 34816 |\n", + "| train/ | |\n", + "| approx_kl | 0.004595418 |\n", + "| clip_fraction | 0.0344 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.28 |\n", + "| explained_variance | 0.662 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.00958 |\n", + "| n_updates | 160 |\n", + "| policy_gradient_loss | -0.0061 |\n", + "| std | 0.859 |\n", + "| value_loss | 0.00385 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.76e+06 |\n", + "| time/ | |\n", + "| fps | 1002 |\n", + "| iterations | 18 |\n", + "| time_elapsed | 36 |\n", + "| total_timesteps | 36864 |\n", + "| train/ | |\n", + "| approx_kl | 0.008695626 |\n", + "| clip_fraction | 0.0789 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.25 |\n", + "| explained_variance | 0.84 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0146 |\n", + "| n_updates | 170 |\n", + "| policy_gradient_loss | -0.00832 |\n", + "| std | 0.835 |\n", + "| value_loss | 0.00851 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.76e+06 |\n", + "| time/ | |\n", + "| fps | 1004 |\n", + "| iterations | 19 |\n", + "| time_elapsed | 38 |\n", + "| total_timesteps | 38912 |\n", + "| train/ | |\n", + "| approx_kl | 0.004202239 |\n", + "| clip_fraction | 0.0506 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.23 |\n", + "| explained_variance | 0.875 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0112 |\n", + "| n_updates | 180 |\n", + "| policy_gradient_loss | -0.00643 |\n", + "| std | 0.826 |\n", + "| value_loss | 0.0049 |\n", + "-----------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.72e+06 |\n", + "| time/ | |\n", + "| fps | 1006 |\n", + "| iterations | 20 |\n", + "| time_elapsed | 40 |\n", + "| total_timesteps | 40960 |\n", + "| train/ | |\n", + "| approx_kl | 0.0056182286 |\n", + "| clip_fraction | 0.044 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.21 |\n", + "| explained_variance | 0.735 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0388 |\n", + "| n_updates | 190 |\n", + "| policy_gradient_loss | -0.00686 |\n", + "| std | 0.801 |\n", + "| value_loss | 0.00517 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.72e+06 |\n", + "| time/ | |\n", + "| fps | 1009 |\n", + "| iterations | 21 |\n", + "| time_elapsed | 42 |\n", + "| total_timesteps | 43008 |\n", + "| train/ | |\n", + "| approx_kl | 0.0044678794 |\n", + "| clip_fraction | 0.0564 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.2 |\n", + "| explained_variance | 0.893 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.00494 |\n", + "| n_updates | 200 |\n", + "| policy_gradient_loss | -0.00672 |\n", + "| std | 0.803 |\n", + "| value_loss | 0.00637 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.68e+06 |\n", + "| time/ | |\n", + "| fps | 987 |\n", + "| iterations | 22 |\n", + "| time_elapsed | 45 |\n", + "| total_timesteps | 45056 |\n", + "| train/ | |\n", + "| approx_kl | 0.0033513391 |\n", + "| clip_fraction | 0.0302 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.19 |\n", + "| explained_variance | 0.765 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.00761 |\n", + "| n_updates | 210 |\n", + "| policy_gradient_loss | -0.0038 |\n", + "| std | 0.794 |\n", + "| value_loss | 0.00508 |\n", + "------------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.68e+06 |\n", + "| time/ | |\n", + "| fps | 964 |\n", + "| iterations | 23 |\n", + "| time_elapsed | 48 |\n", + "| total_timesteps | 47104 |\n", + "| train/ | |\n", + "| approx_kl | 0.004656489 |\n", + "| clip_fraction | 0.0439 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.18 |\n", + "| explained_variance | 0.908 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0369 |\n", + "| n_updates | 220 |\n", + "| policy_gradient_loss | -0.00686 |\n", + "| std | 0.779 |\n", + "| value_loss | 0.00658 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.64e+06 |\n", + "| time/ | |\n", + "| fps | 962 |\n", + "| iterations | 24 |\n", + "| time_elapsed | 51 |\n", + "| total_timesteps | 49152 |\n", + "| train/ | |\n", + "| approx_kl | 0.005987567 |\n", + "| clip_fraction | 0.05 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.16 |\n", + "| explained_variance | 0.723 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.00145 |\n", + "| n_updates | 230 |\n", + "| policy_gradient_loss | -0.00726 |\n", + "| std | 0.765 |\n", + "| value_loss | 0.00448 |\n", + "-----------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.64e+06 |\n", + "| time/ | |\n", + "| fps | 966 |\n", + "| iterations | 25 |\n", + "| time_elapsed | 52 |\n", + "| total_timesteps | 51200 |\n", + "| train/ | |\n", + "| approx_kl | 0.0054580546 |\n", + "| clip_fraction | 0.043 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.15 |\n", + "| explained_variance | 0.901 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.00864 |\n", + "| n_updates | 240 |\n", + "| policy_gradient_loss | -0.00724 |\n", + "| std | 0.766 |\n", + "| value_loss | 0.00696 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.61e+06 |\n", + "| time/ | |\n", + "| fps | 970 |\n", + "| iterations | 26 |\n", + "| time_elapsed | 54 |\n", + "| total_timesteps | 53248 |\n", + "| train/ | |\n", + "| approx_kl | 0.0048291944 |\n", + "| clip_fraction | 0.0397 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.14 |\n", + "| explained_variance | 0.754 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0127 |\n", + "| n_updates | 250 |\n", + "| policy_gradient_loss | -0.0047 |\n", + "| std | 0.745 |\n", + "| value_loss | 0.00429 |\n", + "------------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.61e+06 |\n", + "| time/ | |\n", + "| fps | 973 |\n", + "| iterations | 27 |\n", + "| time_elapsed | 56 |\n", + "| total_timesteps | 55296 |\n", + "| train/ | |\n", + "| approx_kl | 0.006914062 |\n", + "| clip_fraction | 0.0762 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.12 |\n", + "| explained_variance | 0.898 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.0234 |\n", + "| n_updates | 260 |\n", + "| policy_gradient_loss | -0.00764 |\n", + "| std | 0.739 |\n", + "| value_loss | 0.00753 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.58e+06 |\n", + "| time/ | |\n", + "| fps | 976 |\n", + "| iterations | 28 |\n", + "| time_elapsed | 58 |\n", + "| total_timesteps | 57344 |\n", + "| train/ | |\n", + "| approx_kl | 0.004374048 |\n", + "| clip_fraction | 0.0495 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.11 |\n", + "| explained_variance | 0.734 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.00411 |\n", + "| n_updates | 270 |\n", + "| policy_gradient_loss | -0.00574 |\n", + "| std | 0.732 |\n", + "| value_loss | 0.00341 |\n", + "-----------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.58e+06 |\n", + "| time/ | |\n", + "| fps | 979 |\n", + "| iterations | 29 |\n", + "| time_elapsed | 60 |\n", + "| total_timesteps | 59392 |\n", + "| train/ | |\n", + "| approx_kl | 0.006090526 |\n", + "| clip_fraction | 0.0544 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.1 |\n", + "| explained_variance | 0.896 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0111 |\n", + "| n_updates | 280 |\n", + "| policy_gradient_loss | -0.00702 |\n", + "| std | 0.722 |\n", + "| value_loss | 0.00728 |\n", + "-----------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.55e+06 |\n", + "| time/ | |\n", + "| fps | 983 |\n", + "| iterations | 30 |\n", + "| time_elapsed | 62 |\n", + "| total_timesteps | 61440 |\n", + "| train/ | |\n", + "| approx_kl | 0.0043111267 |\n", + "| clip_fraction | 0.0461 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.08 |\n", + "| explained_variance | 0.72 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0198 |\n", + "| n_updates | 290 |\n", + "| policy_gradient_loss | -0.00596 |\n", + "| std | 0.705 |\n", + "| value_loss | 0.00319 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.55e+06 |\n", + "| time/ | |\n", + "| fps | 979 |\n", + "| iterations | 31 |\n", + "| time_elapsed | 64 |\n", + "| total_timesteps | 63488 |\n", + "| train/ | |\n", + "| approx_kl | 0.0050121583 |\n", + "| clip_fraction | 0.0552 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.06 |\n", + "| explained_variance | 0.893 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.00695 |\n", + "| n_updates | 300 |\n", + "| policy_gradient_loss | -0.00873 |\n", + "| std | 0.696 |\n", + "| value_loss | 0.0067 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.55e+06 |\n", + "| time/ | |\n", + "| fps | 965 |\n", + "| iterations | 32 |\n", + "| time_elapsed | 67 |\n", + "| total_timesteps | 65536 |\n", + "| train/ | |\n", + "| approx_kl | 0.0067488514 |\n", + "| clip_fraction | 0.0677 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.05 |\n", + "| explained_variance | 0.653 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0191 |\n", + "| n_updates | 310 |\n", + "| policy_gradient_loss | -0.00957 |\n", + "| std | 0.687 |\n", + "| value_loss | 0.00315 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.52e+06 |\n", + "| time/ | |\n", + "| fps | 950 |\n", + "| iterations | 33 |\n", + "| time_elapsed | 71 |\n", + "| total_timesteps | 67584 |\n", + "| train/ | |\n", + "| approx_kl | 0.0039351527 |\n", + "| clip_fraction | 0.0503 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.04 |\n", + "| explained_variance | 0.89 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.011 |\n", + "| n_updates | 320 |\n", + "| policy_gradient_loss | -0.00724 |\n", + "| std | 0.681 |\n", + "| value_loss | 0.00592 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.52e+06 |\n", + "| time/ | |\n", + "| fps | 951 |\n", + "| iterations | 34 |\n", + "| time_elapsed | 73 |\n", + "| total_timesteps | 69632 |\n", + "| train/ | |\n", + "| approx_kl | 0.0057587875 |\n", + "| clip_fraction | 0.0638 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -1.02 |\n", + "| explained_variance | 0.652 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.00516 |\n", + "| n_updates | 330 |\n", + "| policy_gradient_loss | -0.0065 |\n", + "| std | 0.665 |\n", + "| value_loss | 0.00255 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.49e+06 |\n", + "| time/ | |\n", + "| fps | 954 |\n", + "| iterations | 35 |\n", + "| time_elapsed | 75 |\n", + "| total_timesteps | 71680 |\n", + "| train/ | |\n", + "| approx_kl | 0.0055833566 |\n", + "| clip_fraction | 0.0664 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -0.996 |\n", + "| explained_variance | 0.798 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.0045 |\n", + "| n_updates | 340 |\n", + "| policy_gradient_loss | -0.00726 |\n", + "| std | 0.649 |\n", + "| value_loss | 0.00423 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.49e+06 |\n", + "| time/ | |\n", + "| fps | 958 |\n", + "| iterations | 36 |\n", + "| time_elapsed | 76 |\n", + "| total_timesteps | 73728 |\n", + "| train/ | |\n", + "| approx_kl | 0.0051649846 |\n", + "| clip_fraction | 0.0429 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -0.993 |\n", + "| explained_variance | 0.899 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0105 |\n", + "| n_updates | 350 |\n", + "| policy_gradient_loss | -0.00616 |\n", + "| std | 0.657 |\n", + "| value_loss | 0.00457 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.47e+06 |\n", + "| time/ | |\n", + "| fps | 961 |\n", + "| iterations | 37 |\n", + "| time_elapsed | 78 |\n", + "| total_timesteps | 75776 |\n", + "| train/ | |\n", + "| approx_kl | 0.0059529413 |\n", + "| clip_fraction | 0.0619 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -0.995 |\n", + "| explained_variance | 0.776 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0184 |\n", + "| n_updates | 360 |\n", + "| policy_gradient_loss | -0.00641 |\n", + "| std | 0.652 |\n", + "| value_loss | 0.00389 |\n", + "------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.47e+06 |\n", + "| time/ | |\n", + "| fps | 963 |\n", + "| iterations | 38 |\n", + "| time_elapsed | 80 |\n", + "| total_timesteps | 77824 |\n", + "| train/ | |\n", + "| approx_kl | 0.005639543 |\n", + "| clip_fraction | 0.0458 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -0.996 |\n", + "| explained_variance | 0.915 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.011 |\n", + "| n_updates | 370 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|\n", + "| fps | 956 |\n", + "| iterations | 40 |\n", + "| time_elapsed | 85 |\n", + "| total_timesteps | 81920 |\n", + "| train/ | |\n", + "| approx_kl | 0.0076133907 |\n", + "| clip_fraction | 0.0587 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -0.973 |\n", + "| explained_variance | 0.924 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0182 |\n", + "| n_updates | 390 |\n", + "| policy_gradient_loss | -0.00696 |\n", + "| std | 0.636 |\n", + "| value_loss | 0.00579 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.42e+06 |\n", + "| time/ | |\n", + "| fps | 944 |\n", + "| iterations | 41 |\n", + "| time_elapsed | 88 |\n", + "| total_timesteps | 83968 |\n", + "| train/ | |\n", + "| approx_kl | 0.0061445124 |\n", + "| clip_fraction | 0.0628 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -0.964 |\n", + "| explained_variance | 0.754 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0154 |\n", + "| n_updates | 400 |\n", + "| policy_gradient_loss | -0.00757 |\n", + "| std | 0.634 |\n", + "| value_loss | 0.00352 |\n", + "------------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.42e+06 |\n", + "| time/ | |\n", + "| fps | 933 |\n", + "| iterations | 42 |\n", + "| time_elapsed | 92 |\n", + "| total_timesteps | 86016 |\n", + "| train/ | |\n", + "| approx_kl | 0.0058118524 |\n", + "| clip_fraction | 0.0599 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -0.97 |\n", + "| explained_variance | 0.918 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.024 |\n", + "| n_updates | 410 |\n", + "| policy_gradient_loss | -0.00817 |\n", + "| std | 0.642 |\n", + "| value_loss | 0.00622 |\n", + "------------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.4e+06 |\n", + "| time/ | |\n", + "| fps | 935 |\n", + "| iterations | 43 |\n", + "| time_elapsed | 94 |\n", + "| total_timesteps | 88064 |\n", + "| train/ | |\n", + "| approx_kl | 0.005398696 |\n", + "| clip_fraction | 0.0429 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -0.953 |\n", + "| explained_variance | 0.77 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.00122 |\n", + "| n_updates | 420 |\n", + "| policy_gradient_loss | -0.00627 |\n", + "| std | 0.618 |\n", + "| value_loss | 0.00296 |\n", + "-----------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.4e+06 |\n", + "| time/ | |\n", + "| fps | 937 |\n", + "| iterations | 44 |\n", + "| time_elapsed | 96 |\n", + "| total_timesteps | 90112 |\n", + "| train/ | |\n", + "| approx_kl | 0.005530538 |\n", + "| clip_fraction | 0.0579 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -0.943 |\n", + "| explained_variance | 0.909 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0142 |\n", + "| n_updates | 430 |\n", + "| policy_gradient_loss | -0.00727 |\n", + "| std | 0.623 |\n", + "| value_loss | 0.00661 |\n", + "-----------------------------------------\n", + "------------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.39e+06 |\n", + "| time/ | |\n", + "| fps | 940 |\n", + "| iterations | 45 |\n", + "| time_elapsed | 98 |\n", + "| total_timesteps | 92160 |\n", + "| train/ | |\n", + "| approx_kl | 0.0078549655 |\n", + "| clip_fraction | 0.0686 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -0.947 |\n", + "| explained_variance | 0.765 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.0267 |\n", + "| n_updates | 440 |\n", + "| policy_gradient_loss | -0.00908 |\n", + "| std | 0.625 |\n", + "| value_loss | 0.00305 |\n", + "------------------------------------------\n", + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.39e+06 |\n", + "| time/ | |\n", + "| fps | 940 |\n", + "| iterations | 46 |\n", + "| time_elapsed | 100 |\n", + "| total_timesteps | 94208 |\n", + "| train/ | |\n", + "| approx_kl | 0.005267841 |\n", + "| clip_fraction | 0.0453 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -0.937 |\n", + "| explained_variance | 0.908 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | -0.00631 |\n", + "| n_updates | 450 |\n", + "| policy_gradient_loss | -0.00533 |\n", + "| std | 0.612 |\n", + "| value_loss | 0.00648 |\n", + "-----------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 4.37e+03 |\n", + "| ep_rew_mean | -4.37e+06 |\n", + "| time/ | |\n", + "| fps | 930 |\n", + "| iterations | 47 |\n", + "| time_elapsed | 103 |\n", + "| total_timesteps | 96256 |\n", + "| train/ | |\n", + "| approx_kl | 0.007721955 |\n", + "| clip_fraction | 0.086 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -0.919 |\n", + "| explained_variance | 0.753 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.0058 |\n", + "| n_updates | 460 |\n", + "| policy_gradient_loss | -0.00828 |\n", + "| std | 0.601 |\n", + "| value_loss | 0.00257 |\n", + "-----------------------------------------\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Train :-)\u001b[39;00m\n\u001b[1;32m 2\u001b[0m model \u001b[38;5;241m=\u001b[39m PPO(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMlpPolicy\u001b[39m\u001b[38;5;124m\"\u001b[39m, env, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, gamma\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.95\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlearn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtotal_timesteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m200000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Store the trained Model and environment stats (which are needed as we are standardizing the observations and reward using VecNormalize())\u001b[39;00m\n\u001b[1;32m 5\u001b[0m model\u001b[38;5;241m.\u001b[39msave(RESULT_PATH \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/stable_baselines3/ppo/ppo.py:308\u001b[0m, in \u001b[0;36mPPO.learn\u001b[0;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps, progress_bar)\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlearn\u001b[39m(\n\u001b[1;32m 300\u001b[0m \u001b[38;5;28mself\u001b[39m: SelfPPO,\n\u001b[1;32m 301\u001b[0m total_timesteps: \u001b[38;5;28mint\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 306\u001b[0m progress_bar: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 307\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m SelfPPO:\n\u001b[0;32m--> 308\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlearn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 309\u001b[0m \u001b[43m \u001b[49m\u001b[43mtotal_timesteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtotal_timesteps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 310\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 311\u001b[0m \u001b[43m \u001b[49m\u001b[43mlog_interval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_interval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 312\u001b[0m \u001b[43m \u001b[49m\u001b[43mtb_log_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtb_log_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 313\u001b[0m \u001b[43m \u001b[49m\u001b[43mreset_num_timesteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset_num_timesteps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 314\u001b[0m \u001b[43m \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 315\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/stable_baselines3/common/on_policy_algorithm.py:259\u001b[0m, in \u001b[0;36mOnPolicyAlgorithm.learn\u001b[0;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps, progress_bar)\u001b[0m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39menv \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 258\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_timesteps \u001b[38;5;241m<\u001b[39m total_timesteps:\n\u001b[0;32m--> 259\u001b[0m continue_training \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcollect_rollouts\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrollout_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_rollout_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_steps\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 261\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m continue_training \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/stable_baselines3/common/on_policy_algorithm.py:169\u001b[0m, in \u001b[0;36mOnPolicyAlgorithm.collect_rollouts\u001b[0;34m(self, env, callback, rollout_buffer, n_rollout_steps)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m th\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[1;32m 167\u001b[0m \u001b[38;5;66;03m# Convert to pytorch tensor or to TensorDict\u001b[39;00m\n\u001b[1;32m 168\u001b[0m obs_tensor \u001b[38;5;241m=\u001b[39m obs_as_tensor(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_last_obs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m--> 169\u001b[0m actions, values, log_probs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpolicy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobs_tensor\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 170\u001b[0m actions \u001b[38;5;241m=\u001b[39m actions\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy()\n\u001b[1;32m 172\u001b[0m \u001b[38;5;66;03m# Rescale and perform action\u001b[39;00m\n", + "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/stable_baselines3/common/policies.py:626\u001b[0m, in \u001b[0;36mActorCriticPolicy.forward\u001b[0;34m(self, obs, deterministic)\u001b[0m\n\u001b[1;32m 624\u001b[0m \u001b[38;5;66;03m# Evaluate the values for the given observations\u001b[39;00m\n\u001b[1;32m 625\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue_net(latent_vf)\n\u001b[0;32m--> 626\u001b[0m distribution \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_action_dist_from_latent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlatent_pi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 627\u001b[0m actions \u001b[38;5;241m=\u001b[39m distribution\u001b[38;5;241m.\u001b[39mget_actions(deterministic\u001b[38;5;241m=\u001b[39mdeterministic)\n\u001b[1;32m 628\u001b[0m log_prob \u001b[38;5;241m=\u001b[39m distribution\u001b[38;5;241m.\u001b[39mlog_prob(actions)\n", + "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/stable_baselines3/common/policies.py:656\u001b[0m, in \u001b[0;36mActorCriticPolicy._get_action_dist_from_latent\u001b[0;34m(self, latent_pi)\u001b[0m\n\u001b[1;32m 653\u001b[0m mean_actions \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maction_net(latent_pi)\n\u001b[1;32m 655\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maction_dist, DiagGaussianDistribution):\n\u001b[0;32m--> 656\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maction_dist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mproba_distribution\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmean_actions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_std\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 657\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maction_dist, CategoricalDistribution):\n\u001b[1;32m 658\u001b[0m \u001b[38;5;66;03m# Here mean_actions are the logits before the softmax\u001b[39;00m\n\u001b[1;32m 659\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maction_dist\u001b[38;5;241m.\u001b[39mproba_distribution(action_logits\u001b[38;5;241m=\u001b[39mmean_actions)\n", + "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/stable_baselines3/common/distributions.py:164\u001b[0m, in \u001b[0;36mDiagGaussianDistribution.proba_distribution\u001b[0;34m(self, mean_actions, log_std)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 157\u001b[0m \u001b[38;5;124;03mCreate the distribution given its parameters (mean, std)\u001b[39;00m\n\u001b[1;32m 158\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;124;03m:return:\u001b[39;00m\n\u001b[1;32m 162\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 163\u001b[0m action_std \u001b[38;5;241m=\u001b[39m th\u001b[38;5;241m.\u001b[39mones_like(mean_actions) \u001b[38;5;241m*\u001b[39m log_std\u001b[38;5;241m.\u001b[39mexp()\n\u001b[0;32m--> 164\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdistribution \u001b[38;5;241m=\u001b[39m \u001b[43mNormal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmean_actions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maction_std\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", + "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/torch/distributions/normal.py:56\u001b[0m, in \u001b[0;36mNormal.__init__\u001b[0;34m(self, loc, scale, validate_args)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 55\u001b[0m batch_shape \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloc\u001b[38;5;241m.\u001b[39msize()\n\u001b[0;32m---> 56\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbatch_shape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidate_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_args\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/torch/distributions/distribution.py:75\u001b[0m, in \u001b[0;36mDistribution.__init__\u001b[0;34m(self, batch_shape, event_shape, validate_args)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m valid\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 69\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected parameter \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(value)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m of shape \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtuple\u001b[39m(value\u001b[38;5;241m.\u001b[39mshape)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut found invalid values:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 74\u001b[0m )\n\u001b[0;32m---> 75\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m()\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# Train :-)\n", + "model = PPO(\"MlpPolicy\", env, verbose=1, gamma=0.95)\n", + "model.learn(total_timesteps=200000)\n", + "# Store the trained Model and environment stats (which are needed as we are standardizing the observations and reward using VecNormalize())\n", + "model.save(RESULT_PATH + 'model')\n", + "env.save(RESULT_PATH + 'env.pkl')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "env.save(RESULT_PATH + 'env.pkl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the training process\n", + "training_log = pd.read_csv(RESULT_PATH + 'monitor.csv', skiprows=1)\n", + "training_log['r'].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "load, price, generation = read_data()\n", + "load_eval = load[TEST_INDEX_START:]\n", + "price_eval = price[TEST_INDEX_START:]\n", + "generation_eval = generation[TEST_INDEX_START:]\n", + "\n", + "num_eval_timesteps = TEST_INDEX_END - TEST_INDEX_START\n", + "\n", + "eval_sim = BuildingSimulation(electricity_load_profile=load_eval,\n", + " solar_generation_profile=generation_eval,\n", + " electricity_price=price_eval,\n", + " max_battery_charge_per_timestep=100, \n", + " battery_capacity=400)\n", + "\n", + "eval_env = Environment(eval_sim, num_forecasting_steps=NUM_FORECAST_STEPS, max_timesteps=num_eval_timesteps)\n", + "eval_env = ObservationWrapper(eval_env, NUM_FORECAST_STEPS)\n", + "eval_env = DummyVecEnv([lambda: eval_env])\n", + "# It is important to load the environmental statistics here as we use a rolling mean calculation !\n", + "eval_env = VecNormalize.load(RESULT_PATH + 'env.pkl', eval_env) " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "eval_env.training = False\n", + "\n", + "actions, observations, electricity_consumption, price, rewards = ([], [], [], [], [])\n", + "done = False\n", + "obs = eval_env.reset()\n", + "while not done:\n", + " action = model.predict(obs, deterministic=True)\n", + " obs, r, done, info = eval_env.step([action[0][0]])\n", + "\n", + " actions.append(action[0][0][0])\n", + " original_reward = eval_env.get_original_reward()[0]\n", + " original_obs = eval_env.get_original_obs()[0]\n", + " observations.append(original_obs)\n", + " electricity_consumption.append(info[0]['electricity_consumption'])\n", + " price.append(info[0]['electricity_price'])\n", + " rewards.append(r)\n", + " \n", + "trajectory = pd.DataFrame({\n", + " 'action': actions,\n", + " 'observations': observations,\n", + " 'electricity_consumption': electricity_consumption,\n", + " 'electricity_price': price,\n", + " 'reward': rewards\n", + " }) " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_data = trajectory[200:500]\n", + "observation_df = plot_data['observations'].apply(pd.Series)\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (16,10)\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.plot(observation_df[1], label = 'Residual Load')\n", + "ax.plot(plot_data['electricity_price'], label = 'Electricity Price')\n", + "\n", + "ax1 = ax.twinx()\n", + "ax1.plot(plot_data['action'], label = 'action', color = 'black')\n", + "fig.legend(bbox_to_anchor=[0.5, 0.95], loc = 'center', ncol=5, prop={'size': 16})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compare to Baseline" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "eval_env.training = False\n", + "\n", + "cost = []\n", + "done = False\n", + "obs = eval_env.reset()\n", + "while not done:\n", + " action = model.predict(obs, deterministic=True)\n", + " obs, r, done, info = eval_env.step([action[0][0]])\n", + " cost.append(info[0]['electricity_consumption'] * info[0]['electricity_price'])\n", + "\n", + "cost = sum(cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "eval_env.training = False\n", + "\n", + "baseline_cost = []\n", + "done = False\n", + "obs = eval_env.reset()\n", + "while not done:\n", + " # Always taking noop as action. This is the electricity demand if there would be no battery\n", + " action = [0]\n", + " obs, r, done, info = eval_env.step(action)\n", + " baseline_cost.append(info[0]['electricity_consumption'] * info[0]['electricity_price'])\n", + "\n", + "baseline_cost = sum(baseline_cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.0012597516984353962" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# how much energy did we save by utilizing the battery?\n", + "1 - (cost / baseline_cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9588993.273488251" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "baseline_cost" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9601073.024050813" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cost" + ] + } + ], + "metadata": { + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From f7a65fc56cd31db91e1c14128925ce6ccd63930a Mon Sep 17 00:00:00 2001 From: tobirohrer Date: Fri, 3 Nov 2023 15:38:02 +0100 Subject: [PATCH 2/4] Added MPC and OPC solutions and added more comments --- README.md | 16 + example_solutions/helper.py | 3 + example_solutions/model_predictive_control.py | 79 ++ example_solutions/observation_wrapper.py | 3 + example_solutions/optimal_control_problem.py | 81 ++ ...ement_learning_sample_implementation.ipynb | 1244 +---------------- requirements.txt | 3 +- 7 files changed, 219 insertions(+), 1210 deletions(-) create mode 100644 example_solutions/model_predictive_control.py diff --git a/README.md b/README.md index 2f990f2..89c0853 100644 --- a/README.md +++ b/README.md @@ -97,6 +97,22 @@ The length of the forecast can be defined by setting the parameter `num_forecast The episode ends if the `max_timesteps` of the `Environment()` are reached. +## Example Solutions + +The folder [example_solutions](example_solutions) contains three different example solutions to solve the problem +described. + +1. By applying deep reinforcement learning using the framework [stable-baselines3](https://github.com/DLR-RM/stable-baselines3). +2. By formulating the problem as optimal control problem (OCP) using [pyomo](http://www.pyomo.org/). In this case, it + is assumed that the forecast for the price, load and generation data for the whole period is available. +3. By model predictive control, which solves the optimal control problem formulation from 2. in each time step in a closed loop manner. + In contrast to 2. only a forecast of a fixed length is given in each iteration. + +Note that the execution of the example solutions requires additional dependencies which are not specified inside `setup.py`. +Therefore, make sure to install the required python packages defined in `requirements.txt`. Additionally, an installation +of the `ipopt` solver is required in order to solve the optimal control problem +(by using conda, simply run `conda install -c conda-forge ipopt`). + ## Code Documentation The documentation is available at [https://building-energy-storage-simulation.readthedocs.io/](https://building-energy-storage-simulation.readthedocs.io/en/master/) diff --git a/example_solutions/helper.py b/example_solutions/helper.py index 21e20df..173dde3 100644 --- a/example_solutions/helper.py +++ b/example_solutions/helper.py @@ -6,6 +6,9 @@ TEST_INDEX_START = 4380 TEST_INDEX_END = 8500 +BATTERY_CAPACITY = 400 +BATTERY_POWER = 100 + def read_data() -> Tuple[np.ndarray, np.ndarray, np.ndarray]: load = pd.read_csv('../building_energy_storage_simulation/data/preprocessed/electricity_load_profile.csv')[ diff --git a/example_solutions/model_predictive_control.py b/example_solutions/model_predictive_control.py new file mode 100644 index 0000000..e861265 --- /dev/null +++ b/example_solutions/model_predictive_control.py @@ -0,0 +1,79 @@ +import pyomo.environ as pyo +import numpy as np +import matplotlib.pyplot as plt + +from building_energy_storage_simulation import BuildingSimulation, Environment +from optimal_control_problem import build_optimization_problem +from helper import read_data, TEST_INDEX_END, TEST_INDEX_START, BATTERY_POWER, BATTERY_CAPACITY + +FORECAST_LENGTH = 24 + + +def normalize_to_minus_one_to_one(x, min_value, max_value): + return -1 + 2 * (x - min_value) / (max_value - min_value) + + +solver = pyo.SolverFactory('ipopt') + +load, price, generation = read_data() +load_eval = load[TEST_INDEX_START:] +price_eval = price[TEST_INDEX_START:] +generation_eval = generation[TEST_INDEX_START:] + +num_eval_timesteps = TEST_INDEX_END - TEST_INDEX_START + +sim = BuildingSimulation(electricity_load_profile=load_eval, + solar_generation_profile=generation_eval, + electricity_price=price_eval, + max_battery_charge_per_timestep=BATTERY_POWER, + battery_capacity=BATTERY_CAPACITY) +env = Environment(sim, num_forecasting_steps=FORECAST_LENGTH, max_timesteps=num_eval_timesteps) + +obs, info = env.reset() +done = False + +actions, residual_loads, prices = (np.array([]), np.array([]), np.array([])) + +t = 0 +while not done: + load_forecast = obs[1: FORECAST_LENGTH + 1] + generation_forecast = obs[FORECAST_LENGTH + 1: 2 * FORECAST_LENGTH + 1] + price_forecast = obs[2 * FORECAST_LENGTH + 1: 3 * FORECAST_LENGTH + 1] + residual_load_forecast = load_forecast - generation_forecast + soc = obs[0] + + instance = build_optimization_problem(residual_fixed_load=residual_load_forecast, + price=price_forecast, + soc=soc / BATTERY_CAPACITY * 100, # Convert SOC due to different SOC definitions + battery_capacity=BATTERY_CAPACITY, + battery_power=BATTERY_POWER) + + solver.solve(instance, tee=True) + action = pyo.value(instance.power[0]) + actions = np.append(actions, action) + obs, reward, done, _, info = env.step(normalize_to_minus_one_to_one(action, -1 * BATTERY_POWER, BATTERY_POWER)) + residual_loads = np.append(residual_loads, residual_load_forecast[0]) + prices = np.append(prices, price_forecast[0]) + t += 1 + +baseline_cost = sum(residual_loads[residual_loads > 0] * prices[residual_loads > 0]) +augmented_load = residual_loads + actions +cost = sum(augmented_load[augmented_load > 0] * prices[augmented_load > 0]) + +print('baseline cost: ' + str(baseline_cost)) +print('cost: ' + str(cost)) +print('savings in %: ' + str(cost/baseline_cost)) + +time = range(len(actions)) + +fig1 = plt.figure() +ax = plt.subplot() +ax.plot(residual_loads, label='Residual Load') +ax.plot(residual_loads + actions, label='Augmented Load') +ax.plot(actions, label='Battery Power Applied') +ax.plot(prices, '--', label='Price') +plt.ylabel('Load and Battery Power Applied (kW) & Price (Cent per kWh)') +plt.xlabel('Time Step') +ax.legend() +ax.grid() +plt.show() diff --git a/example_solutions/observation_wrapper.py b/example_solutions/observation_wrapper.py index 382ecbd..9a3f37f 100644 --- a/example_solutions/observation_wrapper.py +++ b/example_solutions/observation_wrapper.py @@ -3,6 +3,9 @@ class ObservationWrapper(gymnasium.Wrapper): + """ + Combines generation and load into one variable to reduce dimensionality of the observation space. + """ def __init__(self, env, forecast_length): super().__init__(env) diff --git a/example_solutions/optimal_control_problem.py b/example_solutions/optimal_control_problem.py index e69de29..eac5764 100644 --- a/example_solutions/optimal_control_problem.py +++ b/example_solutions/optimal_control_problem.py @@ -0,0 +1,81 @@ +import pyomo.environ as pyo +import numpy as np +import matplotlib.pyplot as plt + +from helper import read_data, TEST_INDEX_END, TEST_INDEX_START, BATTERY_CAPACITY, BATTERY_POWER + +DELTA_TIME_HOURS = 1 + + +def build_optimization_problem(residual_fixed_load, price, soc, battery_power, battery_capacity): + # model parameter initilization + time = range(len(residual_fixed_load)) + soc_time = range(len(residual_fixed_load) + 1) + max_power_charge = battery_power + max_power_discharge = -1 * battery_power + max_soc = 100 + min_soc = 0 + soc_init = soc + energy_capacity = battery_capacity + + m = pyo.AbstractModel() + m.power = pyo.Var(time, domain=pyo.Reals, bounds=(max_power_discharge, max_power_charge)) + m.soc = pyo.Var(soc_time, bounds=(min_soc, max_soc)) + + def obj_expression(m): + return sum([price[i] * pyo.log(1 + pyo.exp((m.power[i] + residual_fixed_load[i]))) for i in time]) + + m.OBJ = pyo.Objective(rule=obj_expression, sense=pyo.minimize) + + def soc_start_rule(m): + return m.soc[0] == soc_init + + m.soc_start = pyo.Constraint(rule=soc_start_rule) + + def soc_constraint_rule(m, i): + return m.soc[i + 1] == float(100) * DELTA_TIME_HOURS * (m.power[i]) / energy_capacity + m.soc[i] + + m.soc_constraints = pyo.Constraint(time, rule=soc_constraint_rule) + + return m.create_instance() + + +if __name__ == "__main__": + solver = pyo.SolverFactory('ipopt') + + load, price, generation = read_data() + + load_eval = load[TEST_INDEX_START:TEST_INDEX_END] + price_eval = price[TEST_INDEX_START:TEST_INDEX_END] + generation_eval = generation[TEST_INDEX_START:TEST_INDEX_END] + + residual_fixed_load_eval = load_eval - generation_eval + time = range(len(residual_fixed_load_eval)) + + m = build_optimization_problem(residual_fixed_load_eval, + price_eval, + soc=0, + battery_power=BATTERY_POWER, + battery_capacity=BATTERY_CAPACITY) + solver.solve(m, tee=True) + t = [time[i] * DELTA_TIME_HOURS for i in time] + + baseline_cost = sum(residual_fixed_load_eval[residual_fixed_load_eval > 0] * price_eval[residual_fixed_load_eval > 0]) + augmented_load = residual_fixed_load_eval + np.array([(pyo.value(m.power[i])) for i in time]) + cost = sum(augmented_load[augmented_load > 0] * price_eval[augmented_load > 0]) + + print('baseline cost: ' + str(baseline_cost)) + print('cost: ' + str(cost)) + print('savings in %: ' + str(cost/baseline_cost)) + + fig1 = plt.figure() + ax = plt.subplot() + ax.plot([(residual_fixed_load_eval[i]) for i in time], label='Residual Load') + ax.plot(augmented_load, label='Augmented Load') + ax.plot(price_eval, '--', label='Price') + ax.plot([(pyo.value(m.power[i])) for i in time], label='Battery Power') + plt.ylabel('Load and Battery Power Applied (kW) & Price (Cent per kWh)') + plt.xlabel('Time Step') + ax.legend() + ax.grid() + plt.show() diff --git a/example_solutions/reinforcement_learning_sample_implementation.ipynb b/example_solutions/reinforcement_learning_sample_implementation.ipynb index d301724..912e994 100644 --- a/example_solutions/reinforcement_learning_sample_implementation.ipynb +++ b/example_solutions/reinforcement_learning_sample_implementation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -20,7 +20,7 @@ "from building_energy_storage_simulation import BuildingSimulation, Environment\n", "\n", "from observation_wrapper import ObservationWrapper\n", - "from helper import read_data, TEST_INDEX_START, TEST_INDEX_END" + "from helper import read_data, TEST_INDEX_START, TEST_INDEX_END, BATTERY_CAPACITY, BATTERY_POWER" ] }, { @@ -32,19 +32,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 0. 9.89 9.08 8.22 8.57 8.93 9.2 10.71\n", - " 3.98309 5.005 4.133 4.322 4.546 3.767 3.97 4.059\n", - " 4.326 ]\n" - ] - } - ], + "outputs": [], "source": [ "NUM_FORECAST_STEPS = 8\n", "RESULT_PATH = 'rl_example/'\n", @@ -60,8 +50,8 @@ "sim = BuildingSimulation(electricity_load_profile=load_train,\n", " solar_generation_profile=generation_train,\n", " electricity_price=price_train,\n", - " max_battery_charge_per_timestep=100,\n", - " battery_capacity=400)\n", + " max_battery_charge_per_timestep=BATTERY_POWER,\n", + " battery_capacity=BATTERY_CAPACITY)\n", "\n", "env = Environment(sim, num_forecasting_steps=NUM_FORECAST_STEPS, max_timesteps=len(load_train)-NUM_FORECAST_STEPS)\n", "# ObservationWrapper combines forecast of load and generation to one residual load forecast\n", @@ -72,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -85,1087 +75,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using cpu device\n", - "-----------------------------\n", - "| time/ | |\n", - "| fps | 1846 |\n", - "| iterations | 1 |\n", - "| time_elapsed | 1 |\n", - "| total_timesteps | 2048 |\n", - "-----------------------------\n", - "------------------------------------------\n", - "| time/ | |\n", - "| fps | 1387 |\n", - "| iterations | 2 |\n", - "| time_elapsed | 2 |\n", - "| total_timesteps | 4096 |\n", - "| train/ | |\n", - "| approx_kl | 0.0036777142 |\n", - "| clip_fraction | 0.0229 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.42 |\n", - "| explained_variance | -0.708 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.000247 |\n", - "| n_updates | 10 |\n", - "| policy_gradient_loss | -0.00348 |\n", - "| std | 0.995 |\n", - "| value_loss | 0.234 |\n", - "------------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -5.1e+06 |\n", - "| time/ | |\n", - "| fps | 1261 |\n", - "| iterations | 3 |\n", - "| time_elapsed | 4 |\n", - "| total_timesteps | 6144 |\n", - "| train/ | |\n", - "| approx_kl | 0.0045435634 |\n", - "| clip_fraction | 0.0198 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.4 |\n", - "| explained_variance | 0.323 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.00283 |\n", - "| n_updates | 20 |\n", - "| policy_gradient_loss | -0.00426 |\n", - "| std | 0.97 |\n", - "| value_loss | 0.0201 |\n", - "------------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -5.1e+06 |\n", - "| time/ | |\n", - "| fps | 1205 |\n", - "| iterations | 4 |\n", - "| time_elapsed | 6 |\n", - "| total_timesteps | 8192 |\n", - "| train/ | |\n", - "| approx_kl | 0.004573004 |\n", - "| clip_fraction | 0.0329 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.4 |\n", - "| explained_variance | 0.611 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.00737 |\n", - "| n_updates | 30 |\n", - "| policy_gradient_loss | -0.00455 |\n", - "| std | 0.984 |\n", - "| value_loss | 0.0243 |\n", - "-----------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -5e+06 |\n", - "| time/ | |\n", - "| fps | 1169 |\n", - "| iterations | 5 |\n", - "| time_elapsed | 8 |\n", - "| total_timesteps | 10240 |\n", - "| train/ | |\n", - "| approx_kl | 0.0037469426 |\n", - "| clip_fraction | 0.0484 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.41 |\n", - "| explained_variance | 0.57 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.0261 |\n", - "| n_updates | 40 |\n", - "| policy_gradient_loss | -0.00724 |\n", - "| std | 0.99 |\n", - "| value_loss | 0.00943 |\n", - "------------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -5e+06 |\n", - "| time/ | |\n", - "| fps | 1148 |\n", - "| iterations | 6 |\n", - "| time_elapsed | 10 |\n", - "| total_timesteps | 12288 |\n", - "| train/ | |\n", - "| approx_kl | 0.0058725784 |\n", - "| clip_fraction | 0.0702 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.42 |\n", - "| explained_variance | 0.751 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.0108 |\n", - "| n_updates | 50 |\n", - "| policy_gradient_loss | -0.00806 |\n", - "| std | 1 |\n", - "| value_loss | 0.0147 |\n", - "------------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.93e+06 |\n", - "| time/ | |\n", - "| fps | 1139 |\n", - "| iterations | 7 |\n", - "| time_elapsed | 12 |\n", - "| total_timesteps | 14336 |\n", - "| train/ | |\n", - "| approx_kl | 0.0058474382 |\n", - "| clip_fraction | 0.0539 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.42 |\n", - "| explained_variance | 0.635 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.00986 |\n", - "| n_updates | 60 |\n", - "| policy_gradient_loss | -0.00606 |\n", - "| std | 0.999 |\n", - "| value_loss | 0.00742 |\n", - "------------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.93e+06 |\n", - "| time/ | |\n", - "| fps | 1132 |\n", - "| iterations | 8 |\n", - "| time_elapsed | 14 |\n", - "| total_timesteps | 16384 |\n", - "| train/ | |\n", - "| approx_kl | 0.003978129 |\n", - "| clip_fraction | 0.025 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.42 |\n", - "| explained_variance | 0.82 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.0199 |\n", - "| n_updates | 70 |\n", - "| policy_gradient_loss | -0.00461 |\n", - "| std | 1 |\n", - "| value_loss | 0.00987 |\n", - "-----------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.89e+06 |\n", - "| time/ | |\n", - "| fps | 1127 |\n", - "| iterations | 9 |\n", - "| time_elapsed | 16 |\n", - "| total_timesteps | 18432 |\n", - "| train/ | |\n", - "| approx_kl | 0.0047321245 |\n", - "| clip_fraction | 0.0652 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.4 |\n", - "| explained_variance | 0.685 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.0133 |\n", - "| n_updates | 80 |\n", - "| policy_gradient_loss | -0.00815 |\n", - "| std | 0.97 |\n", - "| value_loss | 0.00682 |\n", - "------------------------------------------\n", - "----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.89e+06 |\n", - "| time/ | |\n", - "| fps | 1125 |\n", - "| iterations | 10 |\n", - "| time_elapsed | 18 |\n", - "| total_timesteps | 20480 |\n", - "| train/ | |\n", - "| approx_kl | 0.00393809 |\n", - "| clip_fraction | 0.0249 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.38 |\n", - "| explained_variance | 0.846 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.0126 |\n", - "| n_updates | 90 |\n", - "| policy_gradient_loss | -0.00404 |\n", - "| std | 0.955 |\n", - "| value_loss | 0.00849 |\n", - "----------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.85e+06 |\n", - "| time/ | |\n", - "| fps | 1122 |\n", - "| iterations | 11 |\n", - "| time_elapsed | 20 |\n", - "| total_timesteps | 22528 |\n", - "| train/ | |\n", - "| approx_kl | 0.0056245844 |\n", - "| clip_fraction | 0.0404 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.37 |\n", - "| explained_variance | 0.752 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.00556 |\n", - "| n_updates | 100 |\n", - "| policy_gradient_loss | -0.00522 |\n", - "| std | 0.941 |\n", - "| value_loss | 0.00499 |\n", - "------------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.85e+06 |\n", - "| time/ | |\n", - "| fps | 1119 |\n", - "| iterations | 12 |\n", - "| time_elapsed | 21 |\n", - "| total_timesteps | 24576 |\n", - "| train/ | |\n", - "| approx_kl | 0.0032175046 |\n", - "| clip_fraction | 0.0312 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.35 |\n", - "| explained_variance | 0.827 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.00478 |\n", - "| n_updates | 110 |\n", - "| policy_gradient_loss | -0.00691 |\n", - "| std | 0.935 |\n", - "| value_loss | 0.00943 |\n", - "------------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.83e+06 |\n", - "| time/ | |\n", - "| fps | 1110 |\n", - "| iterations | 13 |\n", - "| time_elapsed | 23 |\n", - "| total_timesteps | 26624 |\n", - "| train/ | |\n", - "| approx_kl | 0.0033950265 |\n", - "| clip_fraction | 0.0379 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.35 |\n", - "| explained_variance | 0.738 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.00281 |\n", - "| n_updates | 120 |\n", - "| policy_gradient_loss | -0.00417 |\n", - "| std | 0.928 |\n", - "| value_loss | 0.00453 |\n", - "------------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.83e+06 |\n", - "| time/ | |\n", - "| fps | 1054 |\n", - "| iterations | 14 |\n", - "| time_elapsed | 27 |\n", - "| total_timesteps | 28672 |\n", - "| train/ | |\n", - "| approx_kl | 0.005857446 |\n", - "| clip_fraction | 0.0508 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.33 |\n", - "| explained_variance | 0.841 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.00559 |\n", - "| n_updates | 130 |\n", - "| policy_gradient_loss | -0.00754 |\n", - "| std | 0.905 |\n", - "| value_loss | 0.00871 |\n", - "-----------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.79e+06 |\n", - "| time/ | |\n", - "| fps | 1010 |\n", - "| iterations | 15 |\n", - "| time_elapsed | 30 |\n", - "| total_timesteps | 30720 |\n", - "| train/ | |\n", - "| approx_kl | 0.005098461 |\n", - "| clip_fraction | 0.0383 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.31 |\n", - "| explained_variance | 0.684 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.0102 |\n", - "| n_updates | 140 |\n", - "| policy_gradient_loss | -0.00649 |\n", - "| std | 0.895 |\n", - "| value_loss | 0.00368 |\n", - "-----------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.79e+06 |\n", - "| time/ | |\n", - "| fps | 996 |\n", - "| iterations | 16 |\n", - "| time_elapsed | 32 |\n", - "| total_timesteps | 32768 |\n", - "| train/ | |\n", - "| approx_kl | 0.0066045905 |\n", - "| clip_fraction | 0.0508 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.3 |\n", - "| explained_variance | 0.862 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.01 |\n", - "| n_updates | 150 |\n", - "| policy_gradient_loss | -0.00624 |\n", - "| std | 0.885 |\n", - "| value_loss | 0.00755 |\n", - "------------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.79e+06 |\n", - "| time/ | |\n", - "| fps | 999 |\n", - "| iterations | 17 |\n", - "| time_elapsed | 34 |\n", - "| total_timesteps | 34816 |\n", - "| train/ | |\n", - "| approx_kl | 0.004595418 |\n", - "| clip_fraction | 0.0344 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.28 |\n", - "| explained_variance | 0.662 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.00958 |\n", - "| n_updates | 160 |\n", - "| policy_gradient_loss | -0.0061 |\n", - "| std | 0.859 |\n", - "| value_loss | 0.00385 |\n", - "-----------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.76e+06 |\n", - "| time/ | |\n", - "| fps | 1002 |\n", - "| iterations | 18 |\n", - "| time_elapsed | 36 |\n", - "| total_timesteps | 36864 |\n", - "| train/ | |\n", - "| approx_kl | 0.008695626 |\n", - "| clip_fraction | 0.0789 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.25 |\n", - "| explained_variance | 0.84 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.0146 |\n", - "| n_updates | 170 |\n", - "| policy_gradient_loss | -0.00832 |\n", - "| std | 0.835 |\n", - "| value_loss | 0.00851 |\n", - "-----------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.76e+06 |\n", - "| time/ | |\n", - "| fps | 1004 |\n", - "| iterations | 19 |\n", - "| time_elapsed | 38 |\n", - "| total_timesteps | 38912 |\n", - "| train/ | |\n", - "| approx_kl | 0.004202239 |\n", - "| clip_fraction | 0.0506 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.23 |\n", - "| explained_variance | 0.875 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.0112 |\n", - "| n_updates | 180 |\n", - "| policy_gradient_loss | -0.00643 |\n", - "| std | 0.826 |\n", - "| value_loss | 0.0049 |\n", - "-----------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.72e+06 |\n", - "| time/ | |\n", - "| fps | 1006 |\n", - "| iterations | 20 |\n", - "| time_elapsed | 40 |\n", - "| total_timesteps | 40960 |\n", - "| train/ | |\n", - "| approx_kl | 0.0056182286 |\n", - "| clip_fraction | 0.044 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.21 |\n", - "| explained_variance | 0.735 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.0388 |\n", - "| n_updates | 190 |\n", - "| policy_gradient_loss | -0.00686 |\n", - "| std | 0.801 |\n", - 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-0.00696 |\n", - "| std | 0.636 |\n", - "| value_loss | 0.00579 |\n", - "------------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.42e+06 |\n", - "| time/ | |\n", - "| fps | 944 |\n", - "| iterations | 41 |\n", - "| time_elapsed | 88 |\n", - "| total_timesteps | 83968 |\n", - "| train/ | |\n", - "| approx_kl | 0.0061445124 |\n", - "| clip_fraction | 0.0628 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -0.964 |\n", - "| explained_variance | 0.754 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.0154 |\n", - "| n_updates | 400 |\n", - "| policy_gradient_loss | -0.00757 |\n", - "| std | 0.634 |\n", - "| value_loss | 0.00352 |\n", - "------------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.42e+06 |\n", - "| time/ | |\n", - "| fps | 933 |\n", - "| iterations | 42 |\n", - "| time_elapsed | 92 |\n", - "| total_timesteps | 86016 |\n", - "| train/ | |\n", - "| approx_kl | 0.0058118524 |\n", - "| clip_fraction | 0.0599 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -0.97 |\n", - "| explained_variance | 0.918 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.024 |\n", - "| n_updates | 410 |\n", - "| policy_gradient_loss | -0.00817 |\n", - "| std | 0.642 |\n", - "| value_loss | 0.00622 |\n", - "------------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.4e+06 |\n", - "| time/ | |\n", - "| fps | 935 |\n", - "| iterations | 43 |\n", - "| time_elapsed | 94 |\n", - "| total_timesteps | 88064 |\n", - "| train/ | |\n", - "| approx_kl | 0.005398696 |\n", - "| clip_fraction | 0.0429 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -0.953 |\n", - "| explained_variance | 0.77 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.00122 |\n", - "| n_updates | 420 |\n", - "| policy_gradient_loss | -0.00627 |\n", - "| std | 0.618 |\n", - "| value_loss | 0.00296 |\n", - "-----------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.4e+06 |\n", - "| time/ | |\n", - "| fps | 937 |\n", - "| iterations | 44 |\n", - "| time_elapsed | 96 |\n", - "| total_timesteps | 90112 |\n", - "| train/ | |\n", - "| approx_kl | 0.005530538 |\n", - "| clip_fraction | 0.0579 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -0.943 |\n", - "| explained_variance | 0.909 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.0142 |\n", - "| n_updates | 430 |\n", - "| policy_gradient_loss | -0.00727 |\n", - "| std | 0.623 |\n", - "| value_loss | 0.00661 |\n", - "-----------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.39e+06 |\n", - "| time/ | |\n", - "| fps | 940 |\n", - "| iterations | 45 |\n", - "| time_elapsed | 98 |\n", - "| total_timesteps | 92160 |\n", - "| train/ | |\n", - "| approx_kl | 0.0078549655 |\n", - "| clip_fraction | 0.0686 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -0.947 |\n", - "| explained_variance | 0.765 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.0267 |\n", - "| n_updates | 440 |\n", - "| policy_gradient_loss | -0.00908 |\n", - "| std | 0.625 |\n", - "| value_loss | 0.00305 |\n", - "------------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.39e+06 |\n", - "| time/ | |\n", - "| fps | 940 |\n", - "| iterations | 46 |\n", - "| time_elapsed | 100 |\n", - "| total_timesteps | 94208 |\n", - "| train/ | |\n", - "| approx_kl | 0.005267841 |\n", - "| clip_fraction | 0.0453 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -0.937 |\n", - "| explained_variance | 0.908 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.00631 |\n", - "| n_updates | 450 |\n", - "| policy_gradient_loss | -0.00533 |\n", - "| std | 0.612 |\n", - "| value_loss | 0.00648 |\n", - "-----------------------------------------\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 4.37e+03 |\n", - "| ep_rew_mean | -4.37e+06 |\n", - "| time/ | |\n", - "| fps | 930 |\n", - "| iterations | 47 |\n", - "| time_elapsed | 103 |\n", - "| total_timesteps | 96256 |\n", - "| train/ | |\n", - "| approx_kl | 0.007721955 |\n", - "| clip_fraction | 0.086 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -0.919 |\n", - "| explained_variance | 0.753 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.0058 |\n", - "| n_updates | 460 |\n", - "| policy_gradient_loss | -0.00828 |\n", - "| std | 0.601 |\n", - "| value_loss | 0.00257 |\n", - "-----------------------------------------\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Train :-)\u001b[39;00m\n\u001b[1;32m 2\u001b[0m model \u001b[38;5;241m=\u001b[39m PPO(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMlpPolicy\u001b[39m\u001b[38;5;124m\"\u001b[39m, env, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, gamma\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.95\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlearn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtotal_timesteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m200000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Store the trained Model and environment stats (which are needed as we are standardizing the observations and reward using VecNormalize())\u001b[39;00m\n\u001b[1;32m 5\u001b[0m model\u001b[38;5;241m.\u001b[39msave(RESULT_PATH \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/stable_baselines3/ppo/ppo.py:308\u001b[0m, in \u001b[0;36mPPO.learn\u001b[0;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps, progress_bar)\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlearn\u001b[39m(\n\u001b[1;32m 300\u001b[0m \u001b[38;5;28mself\u001b[39m: SelfPPO,\n\u001b[1;32m 301\u001b[0m total_timesteps: \u001b[38;5;28mint\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 306\u001b[0m progress_bar: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 307\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m SelfPPO:\n\u001b[0;32m--> 308\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlearn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 309\u001b[0m \u001b[43m \u001b[49m\u001b[43mtotal_timesteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtotal_timesteps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 310\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 311\u001b[0m \u001b[43m \u001b[49m\u001b[43mlog_interval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_interval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 312\u001b[0m \u001b[43m \u001b[49m\u001b[43mtb_log_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtb_log_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 313\u001b[0m \u001b[43m \u001b[49m\u001b[43mreset_num_timesteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset_num_timesteps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 314\u001b[0m \u001b[43m \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 315\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/stable_baselines3/common/on_policy_algorithm.py:259\u001b[0m, in \u001b[0;36mOnPolicyAlgorithm.learn\u001b[0;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps, progress_bar)\u001b[0m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39menv \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 258\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_timesteps \u001b[38;5;241m<\u001b[39m total_timesteps:\n\u001b[0;32m--> 259\u001b[0m continue_training \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcollect_rollouts\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrollout_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_rollout_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_steps\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 261\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m continue_training \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/stable_baselines3/common/on_policy_algorithm.py:169\u001b[0m, in \u001b[0;36mOnPolicyAlgorithm.collect_rollouts\u001b[0;34m(self, env, callback, rollout_buffer, n_rollout_steps)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m th\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[1;32m 167\u001b[0m \u001b[38;5;66;03m# Convert to pytorch tensor or to TensorDict\u001b[39;00m\n\u001b[1;32m 168\u001b[0m obs_tensor \u001b[38;5;241m=\u001b[39m obs_as_tensor(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_last_obs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m--> 169\u001b[0m actions, values, log_probs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpolicy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobs_tensor\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 170\u001b[0m actions \u001b[38;5;241m=\u001b[39m actions\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy()\n\u001b[1;32m 172\u001b[0m \u001b[38;5;66;03m# Rescale and perform action\u001b[39;00m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1525\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1526\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1530\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/stable_baselines3/common/policies.py:626\u001b[0m, in \u001b[0;36mActorCriticPolicy.forward\u001b[0;34m(self, obs, deterministic)\u001b[0m\n\u001b[1;32m 624\u001b[0m \u001b[38;5;66;03m# Evaluate the values for the given observations\u001b[39;00m\n\u001b[1;32m 625\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue_net(latent_vf)\n\u001b[0;32m--> 626\u001b[0m distribution \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_action_dist_from_latent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlatent_pi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 627\u001b[0m actions \u001b[38;5;241m=\u001b[39m distribution\u001b[38;5;241m.\u001b[39mget_actions(deterministic\u001b[38;5;241m=\u001b[39mdeterministic)\n\u001b[1;32m 628\u001b[0m log_prob \u001b[38;5;241m=\u001b[39m distribution\u001b[38;5;241m.\u001b[39mlog_prob(actions)\n", - "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/stable_baselines3/common/policies.py:656\u001b[0m, in \u001b[0;36mActorCriticPolicy._get_action_dist_from_latent\u001b[0;34m(self, latent_pi)\u001b[0m\n\u001b[1;32m 653\u001b[0m mean_actions \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maction_net(latent_pi)\n\u001b[1;32m 655\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maction_dist, DiagGaussianDistribution):\n\u001b[0;32m--> 656\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maction_dist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mproba_distribution\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmean_actions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_std\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 657\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maction_dist, CategoricalDistribution):\n\u001b[1;32m 658\u001b[0m \u001b[38;5;66;03m# Here mean_actions are the logits before the softmax\u001b[39;00m\n\u001b[1;32m 659\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maction_dist\u001b[38;5;241m.\u001b[39mproba_distribution(action_logits\u001b[38;5;241m=\u001b[39mmean_actions)\n", - "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/stable_baselines3/common/distributions.py:164\u001b[0m, in \u001b[0;36mDiagGaussianDistribution.proba_distribution\u001b[0;34m(self, mean_actions, log_std)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 157\u001b[0m \u001b[38;5;124;03mCreate the distribution given its parameters (mean, std)\u001b[39;00m\n\u001b[1;32m 158\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;124;03m:return:\u001b[39;00m\n\u001b[1;32m 162\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 163\u001b[0m action_std \u001b[38;5;241m=\u001b[39m th\u001b[38;5;241m.\u001b[39mones_like(mean_actions) \u001b[38;5;241m*\u001b[39m log_std\u001b[38;5;241m.\u001b[39mexp()\n\u001b[0;32m--> 164\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdistribution \u001b[38;5;241m=\u001b[39m \u001b[43mNormal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmean_actions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maction_std\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/torch/distributions/normal.py:56\u001b[0m, in \u001b[0;36mNormal.__init__\u001b[0;34m(self, loc, scale, validate_args)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 55\u001b[0m batch_shape \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloc\u001b[38;5;241m.\u001b[39msize()\n\u001b[0;32m---> 56\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbatch_shape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidate_args\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_args\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/building2/lib/python3.10/site-packages/torch/distributions/distribution.py:75\u001b[0m, in \u001b[0;36mDistribution.__init__\u001b[0;34m(self, batch_shape, event_shape, validate_args)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m valid\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 69\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected parameter \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(value)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m of shape \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtuple\u001b[39m(value\u001b[38;5;241m.\u001b[39mshape)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut found invalid values:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 74\u001b[0m )\n\u001b[0;32m---> 75\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m()\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "# Train :-)\n", - "model = PPO(\"MlpPolicy\", env, verbose=1, gamma=0.95)\n", + "model = SAC(\"MlpPolicy\", env, verbose=1, gamma=0.95)\n", "model.learn(total_timesteps=200000)\n", "# Store the trained Model and environment stats (which are needed as we are standardizing the observations and reward using VecNormalize())\n", "model.save(RESULT_PATH + 'model')\n", @@ -1174,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1190,32 +105,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the training process\n", "training_log = pd.read_csv(RESULT_PATH + 'monitor.csv', skiprows=1)\n", @@ -1224,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1238,8 +132,8 @@ "eval_sim = BuildingSimulation(electricity_load_profile=load_eval,\n", " solar_generation_profile=generation_eval,\n", " electricity_price=price_eval,\n", - " max_battery_charge_per_timestep=100, \n", - " battery_capacity=400)\n", + " max_battery_charge_per_timestep=BATTERY_POWER, \n", + " battery_capacity=BATTERY_CAPACITY)\n", "\n", "eval_env = Environment(eval_sim, num_forecasting_steps=NUM_FORECAST_STEPS, max_timesteps=num_eval_timesteps)\n", "eval_env = ObservationWrapper(eval_env, NUM_FORECAST_STEPS)\n", @@ -1250,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1282,43 +176,26 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_data = trajectory[200:500]\n", "observation_df = plot_data['observations'].apply(pd.Series)\n", - "\n", + "augmented_load = observation_df[1] + plot_data['action'] * BATTERY_POWER\n", "plt.rcParams[\"figure.figsize\"] = (16,10)\n", "\n", - "fig, ax = plt.subplots()\n", - "ax.plot(observation_df[1], label = 'Residual Load')\n", - "ax.plot(plot_data['electricity_price'], label = 'Electricity Price')\n", - "\n", - "ax1 = ax.twinx()\n", - "ax1.plot(plot_data['action'], label = 'action', color = 'black')\n", - "fig.legend(bbox_to_anchor=[0.5, 0.95], loc = 'center', ncol=5, prop={'size': 16})" + "fig1 = plt.figure()\n", + "ax = plt.subplot()\n", + "ax.plot(observation_df[1], label='Residual Load')\n", + "ax.plot(augmented_load, label='Augmented Load')\n", + "ax.plot(plot_data['electricity_price'], '--', label='Price')\n", + "ax.plot(plot_data['action']*50, label='Battery Power')\n", + "plt.ylabel('Load and Battery Power Applied (kW) & Price (Cent per kWh)')\n", + "plt.xlabel('Time Step')\n", + "ax.legend()\n", + "ax.grid()\n", + "plt.show()" ] }, { @@ -1330,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1349,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1369,64 +246,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.0012597516984353962" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# how much energy did we save by utilizing the battery?\n", "1 - (cost / baseline_cost)" ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "9588993.273488251" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "baseline_cost" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "9601073.024050813" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cost" - ] } ], "metadata": { diff --git a/requirements.txt b/requirements.txt index 1b9d3ea..70c38da 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,4 +3,5 @@ numpy gymnasium sphinx pytest -stable-baselines3 \ No newline at end of file +stable-baselines3 +pyomo \ No newline at end of file From 636f32214e3d36dbaa7db2b54c3a9d719bf815ca Mon Sep 17 00:00:00 2001 From: tobirohrer Date: Fri, 3 Nov 2023 15:47:46 +0100 Subject: [PATCH 3/4] minor changes --- ...pynb => deep_reinforcement_learning.ipynb} | 0 example_solutions/model_predictive_control.py | 2 +- example_solutions/optimal_control_problem.py | 10 +- ...ement_learning_sample_implementation.ipynb | 221 ------------------ 4 files changed, 5 insertions(+), 228 deletions(-) rename example_solutions/{reinforcement_learning_sample_implementation.ipynb => deep_reinforcement_learning.ipynb} (100%) delete mode 100644 reinforcement_learning_sample_implementation.ipynb diff --git a/example_solutions/reinforcement_learning_sample_implementation.ipynb b/example_solutions/deep_reinforcement_learning.ipynb similarity index 100% rename from example_solutions/reinforcement_learning_sample_implementation.ipynb rename to example_solutions/deep_reinforcement_learning.ipynb diff --git a/example_solutions/model_predictive_control.py b/example_solutions/model_predictive_control.py index e861265..2eafe21 100644 --- a/example_solutions/model_predictive_control.py +++ b/example_solutions/model_predictive_control.py @@ -62,7 +62,7 @@ def normalize_to_minus_one_to_one(x, min_value, max_value): print('baseline cost: ' + str(baseline_cost)) print('cost: ' + str(cost)) -print('savings in %: ' + str(cost/baseline_cost)) +print('savings in %: ' + str(1 - cost/baseline_cost)) time = range(len(actions)) diff --git a/example_solutions/optimal_control_problem.py b/example_solutions/optimal_control_problem.py index eac5764..2d06301 100644 --- a/example_solutions/optimal_control_problem.py +++ b/example_solutions/optimal_control_problem.py @@ -4,10 +4,8 @@ from helper import read_data, TEST_INDEX_END, TEST_INDEX_START, BATTERY_CAPACITY, BATTERY_POWER -DELTA_TIME_HOURS = 1 - -def build_optimization_problem(residual_fixed_load, price, soc, battery_power, battery_capacity): +def build_optimization_problem(residual_fixed_load, price, soc, battery_power, battery_capacity, delta_time_hours=1): # model parameter initilization time = range(len(residual_fixed_load)) soc_time = range(len(residual_fixed_load) + 1) @@ -33,7 +31,7 @@ def soc_start_rule(m): m.soc_start = pyo.Constraint(rule=soc_start_rule) def soc_constraint_rule(m, i): - return m.soc[i + 1] == float(100) * DELTA_TIME_HOURS * (m.power[i]) / energy_capacity + m.soc[i] + return m.soc[i + 1] == float(100) * delta_time_hours * (m.power[i]) / energy_capacity + m.soc[i] m.soc_constraints = pyo.Constraint(time, rule=soc_constraint_rule) @@ -58,7 +56,7 @@ def soc_constraint_rule(m, i): battery_power=BATTERY_POWER, battery_capacity=BATTERY_CAPACITY) solver.solve(m, tee=True) - t = [time[i] * DELTA_TIME_HOURS for i in time] + t = [time[i] for i in time] baseline_cost = sum(residual_fixed_load_eval[residual_fixed_load_eval > 0] * price_eval[residual_fixed_load_eval > 0]) augmented_load = residual_fixed_load_eval + np.array([(pyo.value(m.power[i])) for i in time]) @@ -66,7 +64,7 @@ def soc_constraint_rule(m, i): print('baseline cost: ' + str(baseline_cost)) print('cost: ' + str(cost)) - print('savings in %: ' + str(cost/baseline_cost)) + print('savings in %: ' + str(1 - cost/baseline_cost)) fig1 = plt.figure() ax = plt.subplot() diff --git a/reinforcement_learning_sample_implementation.ipynb b/reinforcement_learning_sample_implementation.ipynb deleted file mode 100644 index b326323..0000000 --- a/reinforcement_learning_sample_implementation.ipynb +++ /dev/null @@ -1,221 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import gymnasium\n", - "import os\n", - "import pandas as pd\n", - "from matplotlib import pyplot as plt\n", - "\n", - "from stable_baselines3 import PPO\n", - "from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize\n", - "from stable_baselines3.common.monitor import Monitor\n", - "\n", - "from building_energy_storage_simulation import BuildingSimulation, Environment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Applying Reiforcement Learning Using Stable Baselines 3\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RL_PATH = 'rl_example/'\n", - "os.makedirs(RL_PATH, exist_ok=True)\n", - "\n", - "# Create Environment\n", - "sim = BuildingSimulation()\n", - "env = Environment(sim)\n", - "initial_obs, info = env.reset()\n", - "print(initial_obs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Wrap with Monitor() so a log of the training is saved \n", - "env = Monitor(env, filename=RL_PATH)\n", - "# Warp with DummyVecEnc() so the observations and reward can be normalized using VecNormalize()\n", - "env = DummyVecEnv([lambda: env])\n", - "env = VecNormalize(env, norm_obs=True, norm_reward=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Train with PPO :-)\n", - "model = PPO(\"MlpPolicy\", env, verbose=1, gamma=0.95)\n", - "model.learn(total_timesteps=50000)\n", - "# Store the trained Model and environment stats (which are needed as we are standardizing the observations and reward using VecNormalize())\n", - "model.save(RL_PATH + 'model')\n", - "env.save(RL_PATH + 'env.pkl')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the training process\n", - "training_log = pd.read_csv(RL_PATH + 'monitor.csv', skiprows=1)\n", - "training_log['r'].plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "env.training = False\n", - "\n", - "actions, observations, electricity_consumption, excess_energy, rewards = ([], [], [], [], [])\n", - "done = False\n", - "obs = env.reset()\n", - "while not done:\n", - " action = model.predict(obs, deterministic=True)\n", - " obs, r, done, info = env.step([action[0][0]])\n", - "\n", - " actions.append(action[0][0][0])\n", - " original_reward = env.get_original_reward()[0]\n", - " original_obs = env.get_original_obs()[0]\n", - " observations.append(original_obs)\n", - " electricity_consumption.append(info[0]['electricity_consumption'])\n", - " excess_energy.append(info[0]['electricity_price'])\n", - " rewards.append(r)\n", - " \n", - "trajectory = pd.DataFrame({\n", - " 'action': actions,\n", - " 'observations': observations,\n", - " 'electricity_consumption': electricity_consumption,\n", - " 'electricity_price': excess_energy,\n", - " 'reward': rewards\n", - " }) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_data = trajectory[0:200]\n", - "observation_df = plot_data['observations'].apply(pd.Series)\n", - "\n", - "plt.rcParams[\"figure.figsize\"] = (16,10)\n", - "\n", - "fig, ax = plt.subplots()\n", - "ax.plot(observation_df[1], label = 'electric load')\n", - "ax.plot(observation_df[5], label = 'solar generation')\n", - "ax.plot(plot_data['electricity_price'], label = 'electricity_price')\n", - "\n", - "ax1 = ax.twinx()\n", - "ax1.plot(plot_data['action'], label = 'action', color = 'black')\n", - "fig.legend(bbox_to_anchor=[0.5, 0.95], loc = 'center', ncol=5, prop={'size': 16})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Compare to Baseline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "env.training = False\n", - "\n", - "cost = []\n", - "done = False\n", - "obs = env.reset()\n", - "while not done:\n", - " action = model.predict(obs, deterministic=True)\n", - " obs, r, done, info = env.step([action[0][0]])\n", - " cost.append(info[0]['electricity_consumption'] * info[0]['electricity_price'])\n", - "\n", - "cost = sum(cost)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "env.training = False\n", - "\n", - "baseline_cost = []\n", - "done = False\n", - "obs = env.reset()\n", - "while not done:\n", - " # Always taking noop as action. This is the electricity demand if there would be no battery\n", - " action = [0]\n", - " obs, r, done, info = env.step(action)\n", - " baseline_cost.append(info[0]['electricity_consumption'] * info[0]['electricity_price'])\n", - "\n", - "baseline_cost = sum(baseline_cost)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# how much energy did we save by utilizing the battery?\n", - "cost / baseline_cost" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "building", - "language": "python", - "name": "building" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 9bf425bd5fbef7e751d51af4ebdfcaa514e621ec Mon Sep 17 00:00:00 2001 From: tobirohrer Date: Fri, 3 Nov 2023 15:55:53 +0100 Subject: [PATCH 4/4] Added more comments --- example_solutions/model_predictive_control.py | 25 +++++++++++-------- example_solutions/optimal_control_problem.py | 3 ++- 2 files changed, 17 insertions(+), 11 deletions(-) diff --git a/example_solutions/model_predictive_control.py b/example_solutions/model_predictive_control.py index 2eafe21..91b4f5f 100644 --- a/example_solutions/model_predictive_control.py +++ b/example_solutions/model_predictive_control.py @@ -42,18 +42,23 @@ def normalize_to_minus_one_to_one(x, min_value, max_value): residual_load_forecast = load_forecast - generation_forecast soc = obs[0] - instance = build_optimization_problem(residual_fixed_load=residual_load_forecast, - price=price_forecast, - soc=soc / BATTERY_CAPACITY * 100, # Convert SOC due to different SOC definitions - battery_capacity=BATTERY_CAPACITY, - battery_power=BATTERY_POWER) - - solver.solve(instance, tee=True) - action = pyo.value(instance.power[0]) - actions = np.append(actions, action) - obs, reward, done, _, info = env.step(normalize_to_minus_one_to_one(action, -1 * BATTERY_POWER, BATTERY_POWER)) + optimization_problem = build_optimization_problem(residual_fixed_load=residual_load_forecast, + price=price_forecast, + soc=soc / BATTERY_CAPACITY * 100, # Convert SOC due to different SOC definitions + battery_capacity=BATTERY_CAPACITY, + battery_power=BATTERY_POWER) + solver.solve(optimization_problem, tee=True) + # Only apply the first action of the optimal solution in each iteration. This is a key concept of MPC. + action = pyo.value(optimization_problem.power[0]) + # Normalize action, as the environment expects normalized actions. + normalized_action = normalize_to_minus_one_to_one(action, -1 * BATTERY_POWER, BATTERY_POWER) + # Apply action to the environment and get new observation aka. state which is used to build the optimal control + # problem of the next time step. + obs, _, done, _, _ = env.step(normalized_action) + residual_loads = np.append(residual_loads, residual_load_forecast[0]) prices = np.append(prices, price_forecast[0]) + actions = np.append(actions, action) t += 1 baseline_cost = sum(residual_loads[residual_loads > 0] * prices[residual_loads > 0]) diff --git a/example_solutions/optimal_control_problem.py b/example_solutions/optimal_control_problem.py index 2d06301..e4be513 100644 --- a/example_solutions/optimal_control_problem.py +++ b/example_solutions/optimal_control_problem.py @@ -6,7 +6,6 @@ def build_optimization_problem(residual_fixed_load, price, soc, battery_power, battery_capacity, delta_time_hours=1): - # model parameter initilization time = range(len(residual_fixed_load)) soc_time = range(len(residual_fixed_load) + 1) max_power_charge = battery_power @@ -21,6 +20,7 @@ def build_optimization_problem(residual_fixed_load, price, soc, battery_power, b m.soc = pyo.Var(soc_time, bounds=(min_soc, max_soc)) def obj_expression(m): + # pyo.log to make the objective expression smooth and therefore solvable return sum([price[i] * pyo.log(1 + pyo.exp((m.power[i] + residual_fixed_load[i]))) for i in time]) m.OBJ = pyo.Objective(rule=obj_expression, sense=pyo.minimize) @@ -31,6 +31,7 @@ def soc_start_rule(m): m.soc_start = pyo.Constraint(rule=soc_start_rule) def soc_constraint_rule(m, i): + # Define the system dynamics as constraint return m.soc[i + 1] == float(100) * delta_time_hours * (m.power[i]) / energy_capacity + m.soc[i] m.soc_constraints = pyo.Constraint(time, rule=soc_constraint_rule)