pfnet/pfrl

Health v2 · maintenance only

Adoption not part of the score

1274 stars · 158 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PFRL is a PyTorch-based deep reinforcement learning library implementing state-of-the-art algorithms such as DQN, Rainbow, PPO, SAC, and A3C. It includes reproducibility scripts and pretrained model zoos for Atari and MuJoCo benchmarks.

Use cases

• train deep RL agents on Atari games
• train continuous control policies with SAC or TD3 on MuJoCo
• reproduce published reinforcement learning benchmark results
• experiment with Rainbow and IQN algorithms in PyTorch
• use pretrained RL models as a starting point
• run asynchronous A3C training on CPU

When to choose

• you want a PyTorch RL library with many classic algorithms implemented
• you need reproducible Atari or MuJoCo benchmark scripts and pretrained models
• you want both discrete and continuous action support with recurrent model options

When to avoid

• you need the newest RL algorithms or active ecosystem like Stable-Baselines3 or RLlib
• you want CPU-asynchronous training for most algorithms (only A3C/ACER support it)
• you need non-PyTorch frameworks such as TensorFlow

Facets

library · maturity maintenance

machine-learning reinforcement-learning sdk reinforcement-learning machine-learning deep-learning python pytorch deep-reinforcement-learning dqn ppo sac a3c atari gym model-zoo linux macos gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="pfnet/pfrl")