dennybritz/reinforcement-learning resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

22116 stars · 6122 forks observed · 2026-08-28

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

A collection of Jupyter Notebook implementations, exercises, and solutions for popular reinforcement learning algorithms, designed to accompany Sutton & Barto's textbook and David Silver's course. Code is written in Python 3 using OpenAI Gym environments and TensorFlow for deep RL methods like DQN and actor-critic.

Use cases

• learn reinforcement learning algorithms with hands-on code
• find solutions to Sutton and Barto textbook exercises
• implement Q-learning and SARSA from scratch
• study deep Q-learning for Atari games
• understand policy gradient and actor-critic methods
• practice with OpenAI Gym environments
• supplement David Silver's RL course with code

When to choose

• you want worked notebook examples alongside classic RL theory materials
• you need reference implementations of tabular and deep RL algorithms
• you're following Sutton's book or Silver's course and want coding practice

When to avoid

• you need a production-ready RL library or framework
• you want the latest state-of-the-art algorithms like PPO or SAC
• you need actively maintained code with current dependency versions

Facets

learning-resource · maturity maintenance

reinforcement-learning machine-learning deep-learning data-science reinforcement-learning machine-learning deep-learning education tutorials python cross-platform jupyter-notebooks openai-gym tensorflow sutton-barto david-silver-course q-learning policy-gradient exercises-and-solutions

Member repositories

For agents

markdown · JSON · MCP: product_card(name="dennybritz/reinforcement-learning")