chainer/chainerrl

Health v2 · maintenance only

Adoption not part of the score

1198 stars · 225 forks observed · 2026-08-28

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

ChainerRL is a deep reinforcement learning library implementing state-of-the-art algorithms like DQN, Rainbow, PPO, SAC, and A3C in Python on top of the Chainer deep learning framework. It includes examples for Atari 2600 and OpenAI Gym environments.

Use cases

• train deep reinforcement learning agents
• implement dqn or rainbow in python
• run a3c async training on atari
• experiment with ppo and sac algorithms
• reproduce rl research results
• learn reinforcement learning with gym environments

When to choose

• you are already using the Chainer framework
• you need CPU asynchronous training algorithms like A3C or ACER
• you want a wide selection of classic deep RL algorithms in one library

When to avoid

• you prefer PyTorch - use PFRL, the maintained successor instead
• you need actively maintained software with recent updates
• you need GPU-first batch training on modern hardware

Facets

library · maturity maintenance

reinforcement-learning deep-learning machine-learning reinforcement-learning machine-learning deep-learning python chainer dqn actor-critic ppo sac a3c gym atari deep-reinforcement-learning linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="chainer/chainerrl")