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RL Baselines Zoo: a Collection of Pre-Trained Reinforcement Learning Agents

A collection of trained Reinforcement Learning (RL) agents, with tuned hyperparameters, using Stable Baselines.

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!

Enjoy a Trained Agent

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 trained_agents/ -n 5000

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

For example (with tensorboard support):

python train.py --algo ppo2 --env CartPole-v1 --tensorboard-log /tmp/stable-baselines/

Train for multiple environments (with one call) and with tensorboard logging:

python train.py --algo a2c --env MountainCar-v0 CartPole-v1 --tensorboard-log /tmp/stable-baselines/

Continue training (here, load pretrained agent for Breakout and continue training for 5000 steps):

python train.py --algo a2c --env BreakoutNoFrameskip-v4 -i trained_agents/a2c/BreakoutNoFrameskip-v4.pkl -n 5000

Record a Video of a Trained Agent

Record 1000 steps:

python -m utils.record_video --algo ppo2 --env BipedalWalkerHardcore-v2 -n 1000

Current Collection: 70+ Trained Agents!

Scores can be found in benchmark.md. To compute them, simply run python -m utils.benchmark.

Atari Games

7 atari games from OpenAI benchmark (NoFrameskip-v4 versions).

RL Algo BeamRider Breakout Enduro Pong Qbert Seaquest SpaceInvaders
A2C ✔️ ✔️ ✔️ ✔️ ✔️ ✔️ ✔️
ACER ✔️ ✔️ ✔️ ✔️ ✔️ ✔️
ACKTR ✔️ ✔️ ✔️ ✔️ ✔️ ✔️ ✔️
PPO2 ✔️ ✔️ ✔️ ✔️ ✔️ ✔️ ✔️
DQN ✔️ ✔️ ✔️ ✔️ ✔️ ✔️ ✔️

Additional Atari Games (to be completed):

RL Algo MsPacman
A2C ✔️
ACER ✔️
ACKTR ✔️
PPO2 ✔️
DQN ✔️

Classic Control Environments

RL Algo CartPole-v1 MountainCar-v0 Acrobot-v1 Pendulum-v0 MountainCarContinuous-v0
A2C ✔️ ✔️ ✔️
ACER ✔️ ✔️ ✔️ N/A N/A
ACKTR ✔️ ✔️ ✔️ N/A N/A
PPO2 ✔️ ✔️ ✔️ ✔️ ✔️
DQN ✔️ ✔️ ✔️ N/A N/A
DDPG N/A N/A N/A ✔️ ✔️

Box2D Environments

RL Algo BipedalWalker-v2 LunarLander-v2 LunarLanderContinuous-v2 BipedalWalkerHardcore-v2 CarRacing-v0
A2C ✔️
ACER N/A ✔️ N/A N/A N/A
ACKTR N/A ✔️ N/A N/A N/A
PPO2 ✔️ ✔️ ✔️ ✔️
DQN N/A ✔️ N/A N/A N/A
DDPG N/A ✔️

PyBullet Environments

See https://github.com/bulletphysics/bullet3/tree/master/examples/pybullet/gym/pybullet_envs. Similar to MuJoCo Envs but with a free simulator: pybullet. We are using BulletEnv-v0 version.

RL Algo Walker2D HalfCheetah Ant Reacher Hopper Humanoid
PPO2 ✔️ ✔️ ✔️ ✔️ ✔️ ✔️
DDPG

PyBullet Envs (Continued)

RL Algo Minitaur MinitaurDuck InvertedDoublePendulum InvertedPendulumSwingup
PPO2 ✔️ ✔️ ✔️ ✔️
DDPG

Colab Notebook: Try it Online!

You can train agents online using colab notebook.

Installation

Stable-Baselines PyPi Package

apt-get install swig cmake libopenmpi-dev zlib1g-dev ffmpeg
pip install stable-baselines==2.2.1 box2d box2d-kengz pyyaml pybullet==2.1.0 pytablewriter

Please see Stable Baselines README for alternatives.

Docker Images

Build docker image (CPU):

docker build . -f docker/Dockerfile.cpu -t rl-baselines-zoo-cpu

GPU:

docker build . -f docker/Dockerfile.gpu -t rl-baselines-zoo

Pull built docker image (CPU):

docker pull araffin/rl-baselines-zoo-cpu

GPU image:

docker pull araffin/rl-baselines-zoo

Run script in the docker image:

./run_docker_cpu.sh python train.py --algo ppo2 --env CartPole-v1

Tests

To run tests, first install pytest, then:

python -m pytest -v tests/

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).

Latest Releases
Video Recorder
 Nov. 23 2018
v0.4
 Nov. 13 2018
v0.3
 Nov. 7 2018
v0.2
 Nov. 6 2018
v0.1
 Nov. 6 2018