google/dopamine

Health v2 · maintenance only

Adoption not part of the score

10900 stars · 1391 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Dopamine is a research framework from Google for fast prototyping of reinforcement learning algorithms, built around a small, easily readable codebase. It provides battle-tested implementations of agents like DQN, C51, Rainbow, IQN, SAC, and PPO using JAX (with legacy TensorFlow support), targeting Atari and Mujoco benchmark environments.

Use cases

• prototype a new reinforcement learning algorithm
• run DQN or Rainbow baseline experiments on Atari
• train a SAC agent on Mujoco continuous control tasks
• reproduce RL benchmark results following Machado et al. recommendations
• learn how classic RL agents are implemented
• experiment with distributional RL ideas like C51 and IQN

When to choose

• you need a compact, hackable codebase for speculative RL research
• you want reproducible benchmark experiments on Atari or Mujoco
• you prefer JAX-based agent implementations
• you want battle-tested reference implementations of DQN, Rainbow, SAC, or PPO

When to avoid

• you need a production RL system rather than a research prototype
• you want a large library with many prebuilt environments and utilities
• you need the newest agents without JAX, since new agents are JAX-only
• you require non-Atari/non-Mujoco environments out of the box

Facets

framework · maturity active

reinforcement-learning machine-learning benchmarking reinforcement-learning machine-learning python jax tensorflow atari mujoco dqn rainbow sac ppo research-framework research linux macos docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="google/dopamine")