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Напрямую из блокнота
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "5.custom_gym_env.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/Alian3785/disciples-ai/blob/master/%D0%9D%D0%B0%D0%BF%D1%80%D1%8F%D0%BC%D1%83%D1%8E%20%D0%B8%D0%B7%20%D0%B1%D0%BB%D0%BE%D0%BA%D0%BD%D0%BE%D1%82%D0%B0\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AoxOjIlOImwx"
},
"source": [
"# Stable Baselines Tutorial - Creating a custom Gym environment\n",
"\n",
"Github repo: https://github.com/araffin/rl-tutorial-jnrr19\n",
"\n",
"Stable-Baselines: https://github.com/hill-a/stable-baselines\n",
"\n",
"Documentation: https://stable-baselines.readthedocs.io/en/master/\n",
"\n",
"RL Baselines zoo: https://github.com/araffin/rl-baselines-zoo\n",
"\n",
"\n",
"## Introduction\n",
"\n",
"In this notebook, you will learn how to use your own environment following the OpenAI Gym interface.\n",
"Once it is done, you can easily use any compatible (depending on the action space) RL algorithm from Stable Baselines on that environment.\n",
"\n",
"## Install Dependencies and Stable Baselines Using Pip\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Sp8rSS4DIhEV",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ed8296a2-0d61-4e04-fb4c-daf771c3b137"
},
"source": [
"# Stable Baselines only supports tensorflow 1.x for now\n",
"\n",
"!pip install stable-baselines3[extra]"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting stable-baselines3[extra]\n",
" Downloading stable_baselines3-1.5.0-py3-none-any.whl (177 kB)\n",
"\u001b[K |████████████████████████████████| 177 kB 5.4 MB/s \n",
"\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]) (3.2.2)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]) (1.3.5)\n",
"Collecting gym==0.21\n",
" Downloading gym-0.21.0.tar.gz (1.5 MB)\n",
"\u001b[K |████████████████████████████████| 1.5 MB 43.1 MB/s \n",
"\u001b[?25hRequirement already satisfied: torch>=1.8.1 in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]) (1.11.0+cu113)\n",
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"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]) (1.21.6)\n",
"Collecting ale-py~=0.7.4\n",
" Downloading ale_py-0.7.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB)\n",
"\u001b[K |████████████████████████████████| 1.6 MB 28.5 MB/s \n",
"\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]) (5.4.8)\n",
"Collecting autorom[accept-rom-license]~=0.4.2\n",
" Downloading AutoROM-0.4.2-py3-none-any.whl (16 kB)\n",
"Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]) (4.1.2.30)\n",
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"Requirement already satisfied: tensorboard>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]) (2.8.0)\n",
"Requirement already satisfied: importlib_metadata>=4.8.1 in /usr/local/lib/python3.7/dist-packages (from gym==0.21->stable-baselines3[extra]) (4.11.3)\n",
"Requirement already satisfied: importlib-resources in /usr/local/lib/python3.7/dist-packages (from ale-py~=0.7.4->stable-baselines3[extra]) (5.7.1)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from autorom[accept-rom-license]~=0.4.2->stable-baselines3[extra]) (7.1.2)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from autorom[accept-rom-license]~=0.4.2->stable-baselines3[extra]) (2.23.0)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from autorom[accept-rom-license]~=0.4.2->stable-baselines3[extra]) (4.64.0)\n",
"Collecting AutoROM.accept-rom-license\n",
" Downloading AutoROM.accept-rom-license-0.4.2.tar.gz (9.8 kB)\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib_metadata>=4.8.1->gym==0.21->stable-baselines3[extra]) (3.8.0)\n",
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"Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->stable-baselines3[extra]) (0.37.1)\n",
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"Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->stable-baselines3[extra]) (0.4.6)\n",
"Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->stable-baselines3[extra]) (1.0.1)\n",
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"Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->stable-baselines3[extra]) (57.4.0)\n",
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"Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->stable-baselines3[extra]) (1.8.1)\n",
"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->stable-baselines3[extra]) (3.3.7)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from absl-py>=0.4->tensorboard>=2.2.0->stable-baselines3[extra]) (1.15.0)\n",
"Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.2.0->stable-baselines3[extra]) (4.8)\n",
"Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.2.0->stable-baselines3[extra]) (4.2.4)\n",
"Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.2.0->stable-baselines3[extra]) (0.2.8)\n",
"Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->stable-baselines3[extra]) (1.3.1)\n",
"Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.2.0->stable-baselines3[extra]) (0.4.8)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->autorom[accept-rom-license]~=0.4.2->stable-baselines3[extra]) (1.24.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->autorom[accept-rom-license]~=0.4.2->stable-baselines3[extra]) (2021.10.8)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->autorom[accept-rom-license]~=0.4.2->stable-baselines3[extra]) (2.10)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->autorom[accept-rom-license]~=0.4.2->stable-baselines3[extra]) (3.0.4)\n",
"Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->stable-baselines3[extra]) (3.2.0)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->stable-baselines3[extra]) (3.0.9)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->stable-baselines3[extra]) (1.4.2)\n",
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"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->stable-baselines3[extra]) (2022.1)\n",
"Building wheels for collected packages: gym, AutoROM.accept-rom-license\n",
" Building wheel for gym (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for gym: filename=gym-0.21.0-py3-none-any.whl size=1616824 sha256=a34da38c03269d0ecaf016a16407f5f70629868b8937690faa99ae3fc6835415\n",
" Stored in directory: /root/.cache/pip/wheels/76/ee/9c/36bfe3e079df99acf5ae57f4e3464ff2771b34447d6d2f2148\n",
" Building wheel for AutoROM.accept-rom-license (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for AutoROM.accept-rom-license: filename=AutoROM.accept_rom_license-0.4.2-py3-none-any.whl size=441027 sha256=fb6eac32ecde059d40004d5e855cde391e6f9eb309a18887bfb7236611dee23a\n",
" Stored in directory: /root/.cache/pip/wheels/87/67/2e/6147e7912fe37f5408b80d07527dab807c1d25f5c403a9538a\n",
"Successfully built gym AutoROM.accept-rom-license\n",
"Installing collected packages: gym, AutoROM.accept-rom-license, autorom, stable-baselines3, ale-py\n",
" Attempting uninstall: gym\n",
" Found existing installation: gym 0.17.3\n",
" Uninstalling gym-0.17.3:\n",
" Successfully uninstalled gym-0.17.3\n",
"Successfully installed AutoROM.accept-rom-license-0.4.2 ale-py-0.7.5 autorom-0.4.2 gym-0.21.0 stable-baselines3-1.5.0\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rzevZcgmJmhi"
},
"source": [
"## First steps with the gym interface\n",
"\n",
"As you have noticed in the previous notebooks, an environment that follows the gym interface is quite simple to use.\n",
"It provides to this user mainly three methods:\n",
"- `reset()` called at the beginning of an episode, it returns an observation\n",
"- `step(action)` called to take an action with the environment, it returns the next observation, the immediate reward, whether the episode is over and additional information\n",
"- (Optional) `render(method='human')` which allow to visualize the agent in action. Note that graphical interface does not work on google colab, so we cannot use it directly (we have to rely on `method='rbg_array'` to retrieve an image of the scene\n",
"\n",
"Under the hood, it also contains two useful properties:\n",
"- `observation_space` which one of the gym spaces (`Discrete`, `Box`, ...) and describe the type and shape of the observation\n",
"- `action_space` which is also a gym space object that describes the action space, so the type of action that can be taken\n",
"\n",
"The best way to learn about gym spaces is to look at the [source code](https://github.com/openai/gym/tree/master/gym/spaces), but you need to know at least the main ones:\n",
"- `gym.spaces.Box`: A (possibly unbounded) box in $R^n$. Specifically, a Box represents the Cartesian product of n closed intervals. Each interval has the form of one of [a, b], (-oo, b], [a, oo), or (-oo, oo). Example: A 1D-Vector or an image observation can be described with the Box space.\n",
"```python\n",
"# Example for using image as input:\n",
"observation_space = spaces.Box(low=0, high=255, shape=(HEIGHT, WIDTH, N_CHANNELS), dtype=np.uint8)\n",
"``` \n",
"\n",
"- `gym.spaces.Discrete`: A discrete space in $\\{ 0, 1, \\dots, n-1 \\}$\n",
" Example: if you have two actions (\"left\" and \"right\") you can represent your action space using `Discrete(2)`, the first action will be 0 and the second 1.\n",
"\n",
"\n",
"\n",
"[Documentation on custom env](https://stable-baselines.readthedocs.io/en/master/guide/custom_env.html)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "I98IKKyNJl6K",
"outputId": "2f48035e-d61f-4573-e9fc-b9120087a1b4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 141
}
},
"source": [
"import gym\n",
"\n",
"env = gym.make(\"CartPole-v1\")\n",
"\n",
"# Box(4,) means that it is a Vector with 4 components\n",
"print(\"Observation space:\", env.observation_space)\n",
"print(\"Shape:\", env.observation_space.shape)\n",
"# Discrete(2) means that there is two discrete actions\n",
"print(\"Action space:\", env.action_space)\n",
"\n",
"# The reset method is called at the beginning of an episode\n",
"obs = env.reset()\n",
"# Sample a random action\n",
"action = env.action_space.sample()\n",
"print(\"Sampled action:\", action)\n",
"obs, reward, done, info = env.step(action)\n",
"# Note the obs is a numpy array\n",
"# info is an empty dict for now but can contain any debugging info\n",
"# reward is a scalar\n",
"print(obs.shape, reward, done, info)\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
" warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Observation space: Box(4,)\n",
"Shape: (4,)\n",
"Action space: Discrete(2)\n",
"Sampled action: 0\n",
"(4,) 1.0 False {}\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RqxatIwPOXe_"
},
"source": [
"## Gym env skeleton\n",
"\n",
"In practice this is how a gym environment looks like.\n",
"Here, we have implemented a simple grid world were the agent must learn to go always left."
]
},
{
"cell_type": "code",
"metadata": {
"id": "rYzDXA9vJfz1"
},
"source": [
"import numpy as np\n",
"import gym\n",
"from gym import spaces\n",
"\n",
"\n",
"class GoLeftEnv(gym.Env):\n",
" \"\"\"\n",
" Custom Environment that follows gym interface.\n",
" This is a simple env where the agent must learn to go always left. \n",
" \"\"\"\n",
" # Because of google colab, we cannot implement the GUI ('human' render mode)\n",
" metadata = {'render.modes': ['console']}\n",
" # Define constants for clearer code\n",
" LEFT = 0\n",
" RIGHT = 1\n",
" HIGH = 2\n",
" LOW = 3\n",
" ATTACK = 4\n",
" def __init__(self, grid_size=10):\n",
" super(GoLeftEnv, self).__init__()\n",
"\n",
" # Size of the 1D-grid\n",
" self.grid_size = grid_size\n",
" # Initialize the agent at the right of the grid\n",
" self.agent_pos = grid_size - 1\n",
"\n",
" # Define action and observation space\n",
" # They must be gym.spaces objects\n",
" # Example when using discrete actions, we have two: left and right\n",
" n_actions = 5\n",
" self.action_space = spaces.Discrete(n_actions)\n",
" # The observation will be the coordinate of the agent\n",
" # this can be described both by Discrete and Box space\n",
" self.observation_space = spaces.Box(low=0, high=100,\n",
" shape=(3,), dtype=np.float32)\n",
"\n",
" def reset(self):\n",
" \"\"\"\n",
" Important: the observation must be a numpy array\n",
" :return: (np.array) \n",
" \"\"\"\n",
" # Initialize the agent at the right of the grid\n",
" self.agent_posX = 1\n",
" self.agent_posY = 1\n",
" self.enemyhealth = 10\n",
" # here we convert to float32 to make it more general (in case we want to use continuous actions)\n",
" return np.array([self.agent_posX, self.agent_posY, self.enemyhealth]).astype(np.float32)\n",
"\n",
" def step(self, action):\n",
" if action == self.LEFT:\n",
" if (self.agent_posX == 1):\n",
" self.agent_posX = 1\n",
" else: \n",
" self.agent_posX -= 1\n",
" elif action == self.RIGHT:\n",
" if (self.agent_posX == 10):\n",
" self.agent_posX = 10\n",
" else: \n",
" self.agent_posX += 1\n",
" elif action == self.LOW:\n",
" if (self.agent_posY == 1):\n",
" self.agent_posY = 1\n",
" else: \n",
" self.agent_posY -= 1\n",
" elif action == self.HIGH:\n",
" if (self.agent_posY == 10):\n",
" self.agent_posY = 10\n",
" else: \n",
" self.agent_posY += 1\n",
" elif action == self.ATTACK:\n",
" if (self.agent_posX == 7 and self.agent_posY == 6):\n",
" self.enemyhealth = 0 \n",
" else:\n",
" raise ValueError(\"Received invalid action={} which is not part of the action space\".format(action))\n",
"\n",
" # Are we at the left of the grid?\n",
" done = bool(self.enemyhealth == 0)\n",
"\n",
" # Null reward everywhere except when reaching the goal (left of the grid)\n",
" reward = 100 if self.enemyhealth == 0 else -1\n",
"\n",
" # Optionally we can pass additional info, we are not using that for now\n",
" info = {}\n",
"\n",
" return np.array([self.agent_posX, self.agent_posY, self.enemyhealth]).astype(np.float32), reward, done, info\n",
"\n",
" def render(self, mode='console'):\n",
" if mode != 'console':\n",
" raise NotImplementedError()\n",
" # agent is represented as a cross, rest as a dot\n",
" print(\"Координата X\")\n",
" print(self.agent_posX)\n",
" print(\"Координата Y\")\n",
" print(self.agent_posY)\n",
"\n",
" def close(self):\n",
" pass\n",
" "
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Zy5mlho1-Ine"
},
"source": [
"### Validate the environment\n",
"\n",
"Stable Baselines provides a [helper](https://stable-baselines.readthedocs.io/en/master/common/env_checker.html) to check that your environment follows the Gym interface. It also optionally checks that the environment is compatible with Stable-Baselines (and emits warning if necessary)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "9DOpP_B0-LXm",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "18904adc-f27f-440a-967b-b44cf82d97bf"
},
"source": [
"from stable_baselines.common.env_checker import check_env"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"WARNING:tensorflow:\n",
"The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
"For more information, please see:\n",
" * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
" * https://github.com/tensorflow/addons\n",
" * https://github.com/tensorflow/io (for I/O related ops)\n",
"If you depend on functionality not listed there, please file an issue.\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1CcUVatq-P0l"
},
"source": [
"env = GoLeftEnv()\n",
"# If the environment don't follow the interface, an error will be thrown\n",
"check_env(env, warn=True)"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "eJ3khFtkSE0g"
},
"source": [
"### Testing the environment"
]
},
{
"cell_type": "code",
"metadata": {
"id": "i62yf2LvSAYY"
},
"source": [
"env = GoLeftEnv(grid_size=10)\n",
"\n",
"obs = env.reset()\n",
"env.render()\n",
"\n",
"print(env.observation_space)\n",
"print(env.action_space)\n",
"print(env.action_space.sample())\n",
"\n",
"GO_LEFT = 0\n",
"# Hardcoded best agent: always go left!\n",
"n_steps = 20\n",
"for step in range(n_steps):\n",
" print(\"Step {}\".format(step + 1))\n",
" obs, reward, done, info = env.step(GO_LEFT)\n",
" print('obs=', obs, 'reward=', reward, 'done=', done)\n",
" env.render()\n",
" if done:\n",
" print(\"Goal reached!\", \"reward=\", reward)\n",
" break"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Pv1e1qJETfHU"
},
"source": [
"### Try it with Stable-Baselines\n",
"\n",
"Once your environment follow the gym interface, it is quite easy to plug in any algorithm from stable-baselines"
]
},
{
"cell_type": "code",
"metadata": {
"id": "PQfLBE28SNDr"
},
"source": [
"from stable_baselines3 import DQN, A2C, HER, TD3, PPO\n",
"from stable_baselines3.common.env_util import make_vec_env\n",
"\n",
"# Instantiate the env\n",
"env = GoLeftEnv(grid_size=10)\n",
"# wrap it\n",
"env = make_vec_env(lambda: env, n_envs=1)"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zRV4Q7FVUKB6",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "dc984709-235e-4e78-8d01-63a9e124a7e2"
},
"source": [
"# Train the agent\n",
"model = DQN('MlpPolicy', env, verbose=1).learn(400000)"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1;30;43mВыходные данные были обрезаны до нескольких последних строк (5000).\u001b[0m\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.0254 |\n",
"| n_updates | 83272 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7692 |\n",
"| fps | 674 |\n",
"| time_elapsed | 568 |\n",
"| total_timesteps | 383142 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.0124 |\n",
"| n_updates | 83285 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7696 |\n",
"| fps | 673 |\n",
"| time_elapsed | 568 |\n",
"| total_timesteps | 383190 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.83 |\n",
"| n_updates | 83297 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7700 |\n",
"| fps | 673 |\n",
"| time_elapsed | 568 |\n",
"| total_timesteps | 383239 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 7.62 |\n",
"| n_updates | 83309 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7704 |\n",
"| fps | 673 |\n",
"| time_elapsed | 568 |\n",
"| total_timesteps | 383287 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.83 |\n",
"| n_updates | 83321 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7708 |\n",
"| fps | 673 |\n",
"| time_elapsed | 568 |\n",
"| total_timesteps | 383341 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.0306 |\n",
"| n_updates | 83335 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7712 |\n",
"| fps | 673 |\n",
"| time_elapsed | 568 |\n",
"| total_timesteps | 383389 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.81 |\n",
"| n_updates | 83347 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.5 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7716 |\n",
"| fps | 673 |\n",
"| time_elapsed | 568 |\n",
"| total_timesteps | 383437 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.81 |\n",
"| n_updates | 83359 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.5 |\n",
"| ep_rew_mean | 88.5 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7720 |\n",
"| fps | 673 |\n",
"| time_elapsed | 569 |\n",
"| total_timesteps | 383486 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.83 |\n",
"| n_updates | 83371 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.5 |\n",
"| ep_rew_mean | 88.5 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7724 |\n",
"| fps | 673 |\n",
"| time_elapsed | 569 |\n",
"| total_timesteps | 383535 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.00757 |\n",
"| n_updates | 83383 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7728 |\n",
"| fps | 673 |\n",
"| time_elapsed | 569 |\n",
"| total_timesteps | 383596 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.82 |\n",
"| n_updates | 83398 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7732 |\n",
"| fps | 673 |\n",
"| time_elapsed | 569 |\n",
"| total_timesteps | 383645 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.8 |\n",
"| n_updates | 83411 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7736 |\n",
"| fps | 673 |\n",
"| time_elapsed | 569 |\n",
"| total_timesteps | 383696 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.81 |\n",
"| n_updates | 83423 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7740 |\n",
"| fps | 673 |\n",
"| time_elapsed | 569 |\n",
"| total_timesteps | 383744 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.8 |\n",
"| n_updates | 83435 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7744 |\n",
"| fps | 673 |\n",
"| time_elapsed | 569 |\n",
"| total_timesteps | 383795 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.00758 |\n",
"| n_updates | 83448 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.7 |\n",
"| ep_rew_mean | 88.3 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7748 |\n",
"| fps | 673 |\n",
"| time_elapsed | 569 |\n",
"| total_timesteps | 383852 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.8 |\n",
"| n_updates | 83462 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7752 |\n",
"| fps | 673 |\n",
"| time_elapsed | 569 |\n",
"| total_timesteps | 383900 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.00733 |\n",
"| n_updates | 83474 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7756 |\n",
"| fps | 673 |\n",
"| time_elapsed | 569 |\n",
"| total_timesteps | 383948 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.0372 |\n",
"| n_updates | 83486 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7760 |\n",
"| fps | 673 |\n",
"| time_elapsed | 570 |\n",
"| total_timesteps | 383999 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.0216 |\n",
"| n_updates | 83499 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7764 |\n",
"| fps | 673 |\n",
"| time_elapsed | 570 |\n",
"| total_timesteps | 384052 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.0205 |\n",
"| n_updates | 83512 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7768 |\n",
"| fps | 673 |\n",
"| time_elapsed | 570 |\n",
"| total_timesteps | 384105 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.8 |\n",
"| n_updates | 83526 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7772 |\n",
"| fps | 673 |\n",
"| time_elapsed | 570 |\n",
"| total_timesteps | 384155 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.0183 |\n",
"| n_updates | 83538 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.6 |\n",
"| ep_rew_mean | 88.4 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7776 |\n",
"| fps | 673 |\n",
"| time_elapsed | 570 |\n",
"| total_timesteps | 384207 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.83 |\n",
"| n_updates | 83551 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.7 |\n",
"| ep_rew_mean | 88.3 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7780 |\n",
"| fps | 673 |\n",
"| time_elapsed | 570 |\n",
"| total_timesteps | 384265 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.0215 |\n",
"| n_updates | 83566 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.7 |\n",
"| ep_rew_mean | 88.3 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7784 |\n",
"| fps | 673 |\n",
"| time_elapsed | 570 |\n",
"| total_timesteps | 384313 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.0165 |\n",
"| n_updates | 83578 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.7 |\n",
"| ep_rew_mean | 88.3 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7788 |\n",
"| fps | 673 |\n",
"| time_elapsed | 570 |\n",
"| total_timesteps | 384361 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.0175 |\n",
"| n_updates | 83590 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.7 |\n",
"| ep_rew_mean | 88.3 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7792 |\n",
"| fps | 673 |\n",
"| time_elapsed | 570 |\n",
"| total_timesteps | 384409 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.82 |\n",
"| n_updates | 83602 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.7 |\n",
"| ep_rew_mean | 88.3 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7796 |\n",
"| fps | 673 |\n",
"| time_elapsed | 570 |\n",
"| total_timesteps | 384459 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.0188 |\n",
"| n_updates | 83614 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.7 |\n",
"| ep_rew_mean | 88.3 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7800 |\n",
"| fps | 673 |\n",
"| time_elapsed | 570 |\n",
"| total_timesteps | 384512 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.0123 |\n",
"| n_updates | 83627 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.7 |\n",
"| ep_rew_mean | 88.3 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7804 |\n",
"| fps | 673 |\n",
"| time_elapsed | 571 |\n",
"| total_timesteps | 384560 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.0129 |\n",
"| n_updates | 83639 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.7 |\n",
"| ep_rew_mean | 88.3 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7808 |\n",
"| fps | 673 |\n",
"| time_elapsed | 571 |\n",
"| total_timesteps | 384610 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.84 |\n",
"| n_updates | 83652 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.7 |\n",
"| ep_rew_mean | 88.3 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7812 |\n",
"| fps | 673 |\n",
"| time_elapsed | 571 |\n",
"| total_timesteps | 384661 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.82 |\n",
"| n_updates | 83665 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.8 |\n",
"| ep_rew_mean | 88.2 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7816 |\n",
"| fps | 673 |\n",
"| time_elapsed | 571 |\n",
"| total_timesteps | 384713 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 0.0325 |\n",
"| n_updates | 83678 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 12.8 |\n",
"| ep_rew_mean | 88.2 |\n",
"| exploration_rate | 0.05 |\n",
"| time/ | |\n",
"| episodes | 7820 |\n",
"| fps | 673 |\n",
"| time_elapsed | 571 |\n",
"| total_timesteps | 384764 |\n",
"| train/ | |\n",
"| learning_rate | 0.0001 |\n",
"| loss | 3.82 |\n",
"| n_updates | 83690 |\n",
"----------------------------------\n",
"----------------------------------\n",