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<!-- [![pipeline status](https://gitlab.com/araffin/rl-baselines3-zoo/badges/master/pipeline.svg)](https://gitlab.com/araffin/rl-baselines3-zoo/-/commits/master) -->RL Baselines3 Zoo: A Training Framework for Stable Baselines3 Reinforcement Learning Agents
<img src="images/car.jpg" align="right" width="40%"/>RL Baselines3 Zoo is a training framework for Reinforcement Learning (RL), using Stable Baselines3.
It provides scripts for training, evaluating agents, tuning hyperparameters, plotting results and recording videos.
In addition, it includes a collection of tuned hyperparameters for common environments and RL algorithms, and agents trained with those settings.
We are looking for contributors to complete the collection!
Goals of this repository:
- Provide a simple interface to train and enjoy RL agents
- Benchmark the different Reinforcement Learning algorithms
- Provide tuned hyperparameters for each environment and RL algorithm
- Have fun with the trained agents!
This is the SB3 version of the original SB2 rl-zoo.
Documentation
Documentation is available online: https://rl-baselines3-zoo.readthedocs.io/
Installation
Minimal installation
From source:
pip install -e .
As a python package:
pip install rl_zoo3
Note: you can do python -m rl_zoo3.train
from any folder and you have access to rl_zoo3
command line interface, for instance, rl_zoo3 train
is equivalent to python train.py
Full installation (with extra envs and test dependencies)
apt-get install swig cmake ffmpeg
pip install -r requirements.txt
pip install -e .[plots,tests]
Please see Stable Baselines3 documentation for alternatives to install stable baselines3.
Train an Agent
The hyperparameters for each environment are defined in hyperparameters/algo_name.yml
.
If the environment exists in this file, then you can train an agent using:
python train.py --algo algo_name --env env_id
Evaluate the agent every 10000 steps using 10 episodes for evaluation (using only one evaluation env):
python train.py --algo sac --env HalfCheetahBulletEnv-v0 --eval-freq 10000 --eval-episodes 10 --n-eval-envs 1
More examples are available in the documentation.
Integrations
The RL Zoo has some integration with other libraries/services like Weights & Biases for experiment tracking or Hugging Face for storing/sharing trained models. You can find out more in the dedicated section of the documentation.
Plot Scripts
Please see the dedicated section of the documentation.
Enjoy a Trained Agent
Note: to download the repo with the trained agents, you must use git clone --recursive https://github.com/DLR-RM/rl-baselines3-zoo
in order to clone the submodule too.
If the trained agent exists, then you can see it in action using:
python enjoy.py --algo algo_name --env env_id
For example, enjoy A2C on Breakout during 5000 timesteps:
python enjoy.py --algo a2c --env BreakoutNoFrameskip-v4 --folder rl-trained-agents/ -n 5000
Hyperparameters Tuning
Please see the dedicated section of the documentation.
Custom Configuration
Please see the dedicated section of the documentation.
Current Collection: 200+ Trained Agents!
Final performance of the trained agents can be found in benchmark.md
. To compute them, simply run python -m rl_zoo3.benchmark
.
List and videos of trained agents can be found on our Huggingface page: https://huggingface.co/sb3
NOTE: this is not a quantitative benchmark as it corresponds to only one run (cf issue #38). This benchmark is meant to check algorithm (maximal) performance, find potential bugs and also allow users to have access to pretrained agents.
Atari Games
7 atari games from OpenAI benchmark (NoFrameskip-v4 versions).
RL Algo | BeamRider | Breakout | Enduro | Pong | Qbert | Seaquest | SpaceInvaders |
---|---|---|---|---|---|---|---|
A2C | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
PPO | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
DQN | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
QR-DQN | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
Additional Atari Games (to be completed):
RL Algo | MsPacman | Asteroids | RoadRunner |
---|---|---|---|
A2C | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
PPO | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
DQN | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
QR-DQN | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
Classic Control Environments
RL Algo | CartPole-v1 | MountainCar-v0 | Acrobot-v1 | Pendulum-v1 | MountainCarContinuous-v0 |
---|---|---|---|---|---|
ARS | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
A2C | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
PPO | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
DQN | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | N/A | N/A |
QR-DQN | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | N/A | N/A |
DDPG | N/A | N/A | N/A | :heavy_check_mark: | :heavy_check_mark: |
SAC | N/A | N/A | N/A | :heavy_check_mark: | :heavy_check_mark: |
TD3 | N/A | N/A | N/A | :heavy_check_mark: | :heavy_check_mark: |
TQC | N/A | N/A | N/A | :heavy_check_mark: | :heavy_check_mark: |
TRPO | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
Box2D Environments
RL Algo | BipedalWalker-v3 | LunarLander-v2 | LunarLanderContinuous-v2 | BipedalWalkerHardcore-v3 | CarRacing-v0 |
---|---|---|---|---|---|
ARS | :heavy_check_mark: | :heavy_check_mark: | |||
A2C | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
PPO | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
DQN | N/A | :heavy_check_mark: | N/A | N/A | N/A |
QR-DQN | N/A | :heavy_check_mark: | N/A | N/A | N/A |
DDPG | :heavy_check_mark: | N/A | :heavy_check_mark: | ||
SAC | :heavy_check_mark: | N/A | :heavy_check_mark: | :heavy_check_mark: | |
TD3 | :heavy_check_mark: | N/A | :heavy_check_mark: | :heavy_check_mark: | |
TQC | :heavy_check_mark: | N/A | :heavy_check_mark: | :heavy_check_mark: | |
TRPO | :heavy_check_mark: | :heavy_check_mark: |
PyBullet Environments
See https://github.com/bulletphysics/bullet3/tree/master/examples/pybullet/gym/pybullet_envs.
Similar to MuJoCo Envs but with a free (MuJoCo 2.1.0+ is now free!) easy to install simulator: pybullet. We are using BulletEnv-v0
version.
Note: those environments are derived from Roboschool and are harder than the Mujoco version (see Pybullet issue)
RL Algo | Walker2D | HalfCheetah | Ant | Reacher | Hopper | Humanoid |
---|---|---|---|---|---|---|
ARS | ||||||
A2C | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
PPO | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
DDPG | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
SAC | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
TD3 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
TQC | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
TRPO | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
PyBullet Envs (Continued)
RL Algo | Minitaur | MinitaurDuck | InvertedDoublePendulum | InvertedPendulumSwingup |
---|---|---|---|---|
A2C | ||||
PPO | ||||
DDPG | ||||
SAC | ||||
TD3 | ||||
TQC |
MuJoCo Environments
RL Algo | Walker2d | HalfCheetah | Ant | Swimmer | Hopper | Humanoid |
---|---|---|---|---|---|---|
ARS | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
A2C | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
PPO | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
DDPG | ||||||
SAC | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
TD3 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
TQC | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
TRPO | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
Robotics Environments
See https://gym.openai.com/envs/#robotics and https://github.com/DLR-RM/rl-baselines3-zoo/pull/71
MuJoCo version: 1.50.1.0 Gym version: 0.18.0
We used the v1 environments.
RL Algo | FetchReach | FetchPickAndPlace | FetchPush | FetchSlide |
---|---|---|---|---|
HER+TQC | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
Panda robot Environments
See https://github.com/qgallouedec/panda-gym/.
Similar to MuJoCo Robotics Envs but with a free easy to install simulator: pybullet.
We used the v1 environments.
RL Algo | PandaReach | PandaPickAndPlace | PandaPush | PandaSlide | PandaStack |
---|---|---|---|---|---|
HER+TQC | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
MiniGrid Envs
See https://github.com/Farama-Foundation/Minigrid. A simple, lightweight and fast Gym environments implementation of the famous gridworld.
RL Algo | Empty-Random-5x5 | FourRooms | DoorKey-5x5 | MultiRoom-N4-S5 | Fetch-5x5-N2 | GoToDoor-5x5 | PutNear-6x6-N2 | RedBlueDoors-6x6 | LockedRoom | KeyCorridorS3R1 | Unlock | ObstructedMaze-2Dlh |
---|---|---|---|---|---|---|---|---|---|---|---|---|
A2C | ||||||||||||
PPO | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
DQN | ||||||||||||
QR-DQN | ||||||||||||
TRPO |
There are 22 environment groups (variations for each) in total.
Colab Notebook: Try it Online!
You can train agents online using Colab notebook.
Passing arguments in an interactive session
The zoo is not meant to be executed from an interactive session (e.g: Jupyter Notebooks, IPython), however, it can be done by modifying sys.argv
and adding the desired arguments.
Example
import sys
from rl_zoo3.train import train
sys.argv = ["python", "--algo", "ppo", "--env", "MountainCar-v0"]
train()
Tests
To run tests, first install pytest, then:
make pytest
Same for type checking with pytype:
make type
Citing the Project
To cite this repository in publications:
@misc{rl-zoo3,
author = {Raffin, Antonin},
title = {RL Baselines3 Zoo},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/DLR-RM/rl-baselines3-zoo}},
}
Contributing
If you trained an agent that is not present in the RL Zoo, please submit a Pull Request (containing the hyperparameters and the score too).
Contributors
We would like to thank our contributors: @iandanforth, @tatsubori @Shade5 @mcres, @ernestum, @qgallouedec