---
id: dopamine-rl
version: "4.1.2"
license: Apache 2.0
license_treatment: permissive
maintenance: active
---

# dopamine-rl — Dopamine: A framework for flexible Reinforcement Learning research

License: permissive · Maintenance: active · Downloads: 98.5K/mo

## What it is and what it does

Dopamine is a research-focused reinforcement learning framework maintained by Google that prioritizes ease of experimentation and code clarity over feature breadth. It implements six well-tested RL agents (DQN, C51, Rainbow, IQN, SAC, PPO) with active development in JAX and legacy support for TensorFlow, designed for researchers and students to prototype new ideas on Atari and Mujoco environments.

The framework emphasizes reproducibility and follows best practices from the RL literature. It ships with 19 dependencies spanning deep learning (TensorFlow, JAX, Flax), environment simulation (Gym, Gymnasium, ALE), and data handling (NumPy, Pandas, OpenCV). Installation from source is recommended for research work because the codebase is designed to be modified directly; pip installation is supported but less typical for active development.

Use it for:

- Prototype and test new RL algorithm ideas on Atari benchmark environments with minimal boilerplate.
- Train and compare standard agents (DQN, Rainbow, PPO) to establish baselines for research papers.
- Learn RL algorithm implementation by reading and modifying well-documented, battle-tested agent code.
- Reproduce published results using Dopamine's implementations following Machado et al. (2018) evaluation protocols.
- Extend existing agents with custom reward shaping or network architectures for domain-specific tasks.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Dopamine is a research framework for prototyping reinforcement learning algorithms, providing implementations of DQN, C51, Rainbow, IQN, SAC, and PPO agents built primarily with JAX.

Yes, if you are conducting RL research or learning algorithm implementation. The framework is actively maintained, carries no known vulnerabilities, and provides a curated set of proven agents with clear code. The 19 dependencies and environment setup overhead make it less suitable for lightweight production inference, but ideal for experimentation and reproducible research.

## Install

pip install dopamine-rl
uv add dopamine-rl
poetry add dopamine-rl

## Installing dopamine-rl

Before you install: Low install friction with a pure-Python wheel, but carries 19 runtime dependencies including TensorFlow, JAX, and environment simulators (Gym, Gymnasium, ALE). Installation from source is recommended over pip for research use.

License in practice: Apache 2.0 permissive license allows free use, modification, and distribution in both open and closed projects without copyleft obligations.

Quickstart:

pip install dopamine-rl

import dopamine
from dopamine.agents import dqn_agent
# Configure and train an agent on Atari environments

Requires manual setup of Atari (ale-py) or Mujoco environments before training; TensorFlow and JAX dependencies have their own system-level prerequisites.

Verify before relying:

- Current Python version support beyond the listed 3.5–3.8 classifiers (requires_python states >=3.5,<4)
- Whether all 19 dependencies are required for basic usage or if subsets suffice for specific agents
- Performance or stability differences between JAX and legacy TensorFlow implementations

## Package facts

- License: Apache 2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 98.5K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

reinforcement learning framework, RL algorithm prototyping, DQN Rainbow PPO implementation, deep reinforcement learning research, JAX RL agents, Atari environment training, policy gradient algorithms, reinforcement-learning, research-framework, jax-based

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