Kaixhin/Rainbow

Health v2 · maintenance only

Adoption not part of the score

1673 stars · 294 forks observed · 2026-08-28

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

A PyTorch implementation of the Rainbow deep reinforcement learning agent, combining DQN improvements like prioritized experience replay, dueling networks, distributional RL, and noisy nets. It trains on Atari games and ships pretrained models in its releases.

Use cases

• train a Rainbow DQN agent on Atari games
• reproduce deep reinforcement learning research results
• compare DQN algorithm variants like double DQN and dueling networks
• run data-efficient reinforcement learning experiments
• download and evaluate pretrained Atari RL models
• learn how distributional RL and noisy nets are implemented in PyTorch

When to choose

• you want a reference implementation of the Rainbow paper in PyTorch
• you need pretrained Atari RL agents for benchmarking
• you are studying or extending DQN-family algorithms

When to avoid

• you need a maintained general-purpose RL library with many environments
• you want production RL training infrastructure
• you need environments beyond Atari without modification

Facets

library · maturity maintenance

reinforcement-learning deep-learning machine-learning reinforcement-learning machine-learning deep-learning python dqn atari pytorch rainbow research-code pretrained-models linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Kaixhin/Rainbow")