CleanRL (Clean Implementation of RL Algorithms)

CleanRL is a Deep Reinforcement Learning library that provides high-quality single-file implementation with research-friendly features. The implementation is clean and simple, yet we can scale it to run thousands of experiments using AWS Batch. The highlight features of CleanRL are:

• 📜 Single-file implementation
  • Every detail about an algorithm variant is put into a single standalone file.
  • For example, our ppo_atari.py only has 340 lines of code but contains all implementation details on how PPO works with Atari games, so it is a great reference implementation to read for folks who do not wish to read an entire modular library.
• 📊 Benchmarked Implementation (7+ algorithms and 34+ games at https://benchmark.cleanrl.dev)
• 📈 Tensorboard Logging
• 🪛 Local Reproducibility via Seeding
• 🎮 Videos of Gameplay Capturing
• 🧫 Experiment Management with Weights and Biases
• 💸 Cloud Integration with docker and AWS

You can read more about CleanRL in our JMLR paper and documentation.

CleanRL only contains implementations of online deep reinforcement learning algorithms. If you are looking for offline algorithms, please check out tinkoff-ai/CORL, which shares a similar design philosophy as CleanRL.

ℹ️ Support for Gymnasium: Farama-Foundation/Gymnasium is the next generation of openai/gym that will continue to be maintained and introduce new features. Please see their announcement for further detail. We are migrating to gymnasium and the progress can be tracked in vwxyzjn/cleanrl#277.

⚠️ NOTE: CleanRL is not a modular library and therefore it is not meant to be imported. At the cost of duplicate code, we make all implementation details of a DRL algorithm variant easy to understand, so CleanRL comes with its own pros and cons. You should consider using CleanRL if you want to 1) understand all implementation details of an algorithm's varaint or 2) prototype advanced features that other modular DRL libraries do not support (CleanRL has minimal lines of code so it gives you great debugging experience and you don't have do a lot of subclassing like sometimes in modular DRL libraries).

Get started

Prerequisites:

• Python >=3.7.1,<3.11
• Poetry 1.2.1+

To run experiments locally, give the following a try:

git clone https://github.com/vwxyzjn/cleanrl.git && cd cleanrl
poetry install

# alternatively, you could use `poetry shell` and do
# `python run cleanrl/ppo.py`
poetry run python cleanrl/ppo.py \
    --seed 1 \
    --env-id CartPole-v0 \
    --total-timesteps 50000

# open another temrminal and enter `cd cleanrl/cleanrl`
tensorboard --logdir runs

To use experiment tracking with wandb, run

wandb login # only required for the first time
poetry run python cleanrl/ppo.py \
    --seed 1 \
    --env-id CartPole-v0 \
    --total-timesteps 50000 \
    --track \
    --wandb-project-name cleanrltest

If you are not using poetry, you can install CleanRL with requirements.txt:

# core dependencies
pip install -r requirements/requirements.txt

# optional dependencies
pip install -r requirements/requirements-atari.txt
pip install -r requirements/requirements-mujoco.txt
pip install -r requirements/requirements-mujoco_py.txt
pip install -r requirements/requirements-procgen.txt
pip install -r requirements/requirements-envpool.txt
pip install -r requirements/requirements-pettingzoo.txt
pip install -r requirements/requirements-jax.txt
pip install -r requirements/requirements-docs.txt
pip install -r requirements/requirements-cloud.txt

To run training scripts in other games:

poetry shell

# classic control
python cleanrl/dqn.py --env-id CartPole-v1
python cleanrl/ppo.py --env-id CartPole-v1
python cleanrl/c51.py --env-id CartPole-v1

# atari
poetry install -E atari
python cleanrl/dqn_atari.py --env-id BreakoutNoFrameskip-v4
python cleanrl/c51_atari.py --env-id BreakoutNoFrameskip-v4
python cleanrl/ppo_atari.py --env-id BreakoutNoFrameskip-v4
python cleanrl/sac_atari.py --env-id BreakoutNoFrameskip-v4

# NEW: 3-4x side-effects free speed up with envpool's atari (only available to linux)
poetry install -E envpool
python cleanrl/ppo_atari_envpool.py --env-id BreakoutNoFrameskip-v4
# Learn Pong-v5 in ~5-10 mins
# Side effects such as lower sample efficiency might occur
poetry run python ppo_atari_envpool.py --clip-coef=0.2 --num-envs=16 --num-minibatches=8 --num-steps=128 --update-epochs=3

# procgen
poetry install -E procgen
python cleanrl/ppo_procgen.py --env-id starpilot
python cleanrl/ppg_procgen.py --env-id starpilot

# ppo + lstm
poetry install -E atari
python cleanrl/ppo_atari_lstm.py --env-id BreakoutNoFrameskip-v4

You may also use a prebuilt development environment hosted in Gitpod:

Algorithms Implemented

Algorithm	Variants Implemented
✅ Proximal Policy Gradient (PPO)	ppo.py, docs
	ppo_atari.py, docs
	ppo_continuous_action.py, docs
	ppo_atari_lstm.py, docs
	ppo_atari_envpool.py, docs
	ppo_atari_envpool_xla_jax.py, docs
	ppo_atari_envpool_xla_jax_scan.py, docs)
	ppo_procgen.py, docs
	ppo_atari_multigpu.py, docs
	ppo_pettingzoo_ma_atari.py, docs
	ppo_continuous_action_isaacgym.py, docs
✅ Deep Q-Learning (DQN)	dqn.py, docs
	dqn_atari.py, docs
	dqn_jax.py, docs
	dqn_atari_jax.py, docs
✅ Categorical DQN (C51)	c51.py, docs
	c51_atari.py, docs
	c51_jax.py, docs
	c51_atari_jax.py, docs
✅ Soft Actor-Critic (SAC)	sac_continuous_action.py, docs
	sac_atari.py, docs
✅ Deep Deterministic Policy Gradient (DDPG)	ddpg_continuous_action.py, docs
	ddpg_continuous_action_jax.py, docs
✅ Twin Delayed Deep Deterministic Policy Gradient (TD3)	td3_continuous_action.py, docs
	td3_continuous_action_jax.py, docs
✅ Phasic Policy Gradient (PPG)	ppg_procgen.py, docs
✅ Random Network Distillation (RND)	ppo_rnd_envpool.py, docs
✅ Qdagger	qdagger_dqn_atari_impalacnn.py, docs
	qdagger_dqn_atari_jax_impalacnn.py, docs

Open RL Benchmark

To make our experimental data transparent, CleanRL participates in a related project called Open RL Benchmark, which contains tracked experiments from popular DRL libraries such as ours, Stable-baselines3, openai/baselines, jaxrl, and others.

Check out https://benchmark.cleanrl.dev/ for a collection of Weights and Biases reports showcasing tracked DRL experiments. The reports are interactive, and researchers can easily query information such as GPU utilization and videos of an agent's gameplay that are normally hard to acquire in other RL benchmarks. In the future, Open RL Benchmark will likely provide an dataset API for researchers to easily access the data (see repo).

Support and get involved

We have a Discord Community for support. Feel free to ask questions. Posting in Github Issues and PRs are also welcome. Also our past video recordings are available at YouTube

Citing CleanRL

If you use CleanRL in your work, please cite our technical paper:

@article{huang2022cleanrl,
  author  = {Shengyi Huang and Rousslan Fernand Julien Dossa and Chang Ye and Jeff Braga and Dipam Chakraborty and Kinal Mehta and João G.M. Araújo},
  title   = {CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms},
  journal = {Journal of Machine Learning Research},
  year    = {2022},
  volume  = {23},
  number  = {274},
  pages   = {1--18},
  url     = {http://jmlr.org/papers/v23/21-1342.html}
}