DLR-RM / rl-baselines3-zoo

A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

RL Baselines3 Zoo: A Training Framework for Stable Baselines3 Reinforcement Learning Agents

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:

1. Provide a simple interface to train and enjoy RL agents
2. Benchmark the different Reinforcement Learning algorithms
3. Provide tuned hyperparameters for each environment and RL algorithm
4. 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	✔️	✔️	✔️	✔️	✔️	✔️	✔️
PPO	✔️	✔️	✔️	✔️	✔️	✔️	✔️
DQN	✔️	✔️	✔️	✔️	✔️	✔️	✔️
QR-DQN	✔️	✔️	✔️	✔️	✔️	✔️	✔️

Additional Atari Games (to be completed):

RL Algo	MsPacman	Asteroids	RoadRunner
A2C	✔️	✔️	✔️
PPO	✔️	✔️	✔️
DQN	✔️	✔️	✔️
QR-DQN	✔️	✔️	✔️

Classic Control Environments

RL Algo	CartPole-v1	MountainCar-v0	Acrobot-v1	Pendulum-v1	MountainCarContinuous-v0
ARS	✔️	✔️	✔️	✔️	✔️
A2C	✔️	✔️	✔️	✔️	✔️
PPO	✔️	✔️	✔️	✔️	✔️
DQN	✔️	✔️	✔️	N/A	N/A
QR-DQN	✔️	✔️	✔️	N/A	N/A
DDPG	N/A	N/A	N/A	✔️	✔️
SAC	N/A	N/A	N/A	✔️	✔️
TD3	N/A	N/A	N/A	✔️	✔️
TQC	N/A	N/A	N/A	✔️	✔️
TRPO	✔️	✔️	✔️	✔️	✔️

Box2D Environments

RL Algo	BipedalWalker-v3	LunarLander-v2	LunarLanderContinuous-v2	BipedalWalkerHardcore-v3	CarRacing-v0
ARS		✔️		✔️
A2C	✔️	✔️	✔️	✔️
PPO	✔️	✔️	✔️	✔️
DQN	N/A	✔️	N/A	N/A	N/A
QR-DQN	N/A	✔️	N/A	N/A	N/A
DDPG	✔️	N/A	✔️
SAC	✔️	N/A	✔️	✔️
TD3	✔️	N/A	✔️	✔️
TQC	✔️	N/A	✔️	✔️
TRPO		✔️	✔️

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	✔️	✔️	✔️	✔️	✔️
PPO	✔️	✔️	✔️	✔️	✔️
DDPG	✔️	✔️	✔️	✔️	✔️
SAC	✔️	✔️	✔️	✔️	✔️
TD3	✔️	✔️	✔️	✔️	✔️
TQC	✔️	✔️	✔️	✔️	✔️
TRPO	✔️	✔️	✔️	✔️	✔️

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	✔️	✔️	✔️	✔️	✔️
A2C	✔️	✔️	✔️	✔️	✔️	✔️
PPO	✔️	✔️	✔️	✔️	✔️
DDPG
SAC	✔️	✔️	✔️	✔️	✔️	✔️
TD3	✔️	✔️	✔️	✔️	✔️	✔️
TQC	✔️	✔️	✔️	✔️	✔️	✔️
TRPO	✔️	✔️	✔️	✔️	✔️

Robotics Environments

See https://gym.openai.com/envs/#robotics and #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	✔️	✔️	✔️	✔️

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	✔️	✔️	✔️	✔️	✔️

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	✔️	✔️	✔️	✔️	✔️	✔️	✔️	✔️	✔️	✔️	✔️	✔️
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