cleanrl

---

name: cleanrl description: Single-file deep reinforcement learning implementations (CleanRL). High-quality standalone implementations of PPO, DQN, C51, SAC, DDPG, TD3 with research-friendly features. Each algorithm is a self-contained file with ~300-500 lines. Includes Atari, MuJoCo, Procgen, PettingZoo multi-agent, and JAX variants. Use for RL algorithm reference, rapid prototyping, and understanding implementation details. license: MIT license tags: [ppo, dqn, sac, rl-reference-implementation, cleanrl] metadata: skill-author: K-Dense Inc. --------|------|----------------| | PPO | ppo.py | python cleanrl/ppo.py --env-id CartPole-v1 | | PPO Atari | ppo_atari.py | python cleanrl/ppo_atari.py --env-id BreakoutNoFrameskip-v4 | | PPO Continuous | ppo_continuous_action.py | python cleanrl/ppo_continuous_action.py --env-id HalfCheetah-v4 | | PPO Multi-Agent | ppo_pettingzoo_ma_atari.py | python cleanrl/ppo_pettingzoo_ma_atari.py --env-id pong_v3 | | DQN | dqn.py | python cleanrl/dqn.py --env-id CartPole-v1 | | DQN Atari | dqn_atari.py | python cleanrl/dqn_atari.py --env-id BreakoutNoFrameskip-v4 | | C51 Atari | c51_atari.py | python cleanrl/c51_atari.py --env-id BreakoutNoFrameskip-v4 | | SAC Continuous | sac_continuous_action.py | python cleanrl/sac_continuous_action.py --env-id HalfCheetah-v4 | | SAC Atari | sac_atari.py | python cleanrl/sac_atari.py --env-id BreakoutNoFrameskip-v4 | | DDPG | ddpg_continuous_action.py | python cleanrl/ddpg_continuous_action.py --env-id HalfCheetah-v4 | | TD3 | td3_continuous_action.py | python cleanrl/td3_continuous_action.py --env-id HalfCheetah-v4 |

3. PPO Training Workflow

# Minimal PPO on CartPole
python cleanrl/ppo.py \
    --seed 1 \
    --env-id CartPole-v1 \
    --total-timesteps 50000 \
    --track \
    --wandb-project-name my-project

# PPO on Atari (standard config)
python cleanrl/ppo_atari.py \
    --seed 1 \
    --env-id BreakoutNoFrameskip-v4 \
    --total-timesteps 10000000 \
    --track \
    --capture-video

# PPO on MuJoCo continuous control
python cleanrl/ppo_continuous_action.py \
    --seed 1 \
    --env-id HalfCheetah-v4 \
    --total-timesteps 1000000

Key PPO Hyperparameters:

Parameter	CartPole/Classic	Atari	MuJoCo
--total-timesteps	50K	10M	1M
--learning-rate	2.5e-4	2.5e-4	3e-4
--num-envs	4	8	1
--num-steps	128	128	2048
--anneal-lr	True	True	False
--gae-lambda	0.95	0.95	0.95
--update-epochs	4	4	10
--norm-adv	True	True	True
--clip-coef	0.2	0.1	0.2
--ent-coef	0.01	0.01	0.0

4. DQN Training

# DQN on Atari
python cleanrl/dqn_atari.py \
    --seed 1 \
    --env-id BreakoutNoFrameskip-v4 \
    --total-timesteps 10000000 \
    --buffer-size 100000 \
    --learning-starts 80000 \
    --target-network-frequency 1000 \
    --batch-size 32 \
    --track

5. Multi-Agent RL with PettingZoo

# PPO on multi-agent Atari Pong
python cleanrl/ppo_pettingzoo_ma_atari.py \
    --seed 1 \
    --env-id pong_v3 \
    --total-timesteps 10000000 \
    --track

# Available MA environments:
# pong_v3, surround_v2, tennis_v3, space_invaders_v2,
# warlords_v3, combat_plane_v2, combat_tank_v2

6. Logging and Monitoring

# TensorBoard (runs in cleanrl/runs/)
tensorboard --logdir runs

# Weights & Biases (requires wandb login)
python cleanrl/ppo.py --track --wandb-project-name my-project --wandb-entity my-entity

# Video capture (every 100th evaluation)
python cleanrl/ppo_atari.py --capture-video --env-id BreakoutNoFrameskip-v4

7. JAX-Accelerated Variants

5-10x faster training via JAX compilation + EnvPool:

# Install JAX support
pip install -r requirements/requirements-jax.txt

# JAX PPO on Atari (ultra-fast)
python cleanrl/ppo_atari_envpool_xla_jax.py \
    --env-id BreakoutNoFrameskip-v4 \
    --total-timesteps 10000000

# JAX DQN on Atari
python cleanrl/dqn_atari_jax.py \
    --env-id BreakoutNoFrameskip-v4 \
    --total-timesteps 10000000

8. Docker and Cloud (AWS)

# Build Docker image
docker build -t cleanrl .

# Submit to AWS Batch
python cleanrl/ppo_atari.py \
    --env-id BreakoutNoFrameskip-v4 \
    --total-timesteps 10000000 \
    --track \
    --upload-model

9. Algorithm Structure (Reading an Implementation)

Each file follows a consistent structure:

# 1. Imports
# 2. parse_args() — CLI arguments
# 3. make_env() — Environment creation
# 4. Agent class (if needed) — Neural network, usually simple MLP/CNN
# 5. main():
#    a. Setup: seeding, device, envs
#    b. Initialize agent, optimizer
#    c. Initialize storage (rollout buffer, replay buffer)
#    d. Training loop:
#       - Collect experience
#       - Compute returns/advantages
#       - Update policy/value/Q-network
#       - Log metrics
#    e. Save model, upload

Each file is ~300-500 lines and is meant to be read top-to-bottom.

10. Debugging and Development

# Minimal test run (fewer steps, more frequent logging)
python cleanrl/ppo.py \
    --env-id CartPole-v1 \
    --total-timesteps 5000 \
    --num-envs 1 \
    --num-steps 32 \
    --track

# Disable wandb (pure TensorBoard)
python cleanrl/ppo.py --env-id CartPole-v1 --total-timesteps 50000

# Check available env IDs
python -c "import gymnasium as gym; print([e for e in gym.envs.registry if 'CartPole' in e])"

Key Patterns

1. CleanRL is NOT a library — don't import cleanrl, run the scripts directly
2. Each file is self-contained — copy ppo.py and modify it for your research
3. Use --track for W&B logging, omit for plain TensorBoard
4. --capture-video saves agent gameplay — great for qualitative evaluation
5. JAX variants are fastest but require understanding of jax.lax.scan
6. All implementations are benchmarked — see https://benchmark.cleanrl.dev

References

• CleanRL Documentation
• Algorithm Benchmarks
• JMLR Paper
• CORL (offline RL fork)
• LeanRL (optimized PyTorch fork)