awesome-deep-rl
github.com/kengz/awesome-deep-rl ↗A curated list of awesome Deep Reinforcement Learning resources.
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me timeline resources from awesome-deep-rl"
Installation instructions →What's inside
Timeline
Libraries
- AgileRL
A Deep Reinforcement Learning library focused on improving development by introducing RLOps - MLOps for reinforcement learning.
- Berkeley Ray RLLib
An open-source library for reinforcement learning that offers both high scalability and a unified API for a variety of applications.
- Berkeley Softlearning
A reinforcement learning framework for training maximum entropy policies in continuous domains.
- Catalyst
Accelerated DL & RL.
- ChainerRL
A deep reinforcement learning library built on top of Chainer.
- d3rlpy
An offline deep reinforcement learning library.
Environments
- AI2-THOR
A near photo-realistic interactable framework for AI agents.
- Animal-AI Olympics
An AI competition with tests inspired by animal cognition.
- Berkeley rl-generalization
Modifiable OpenAI Gym environments for studying generalization in RL.
- BTGym
Scalable event-driven RL-friendly backtesting library. Build on top of Backtrader with OpenAI Gym environment API.
- Carla
Open-source simulator for autonomous driving research.
- CuLE
A CUDA port of the Atari Learning Environment (ALE).
Books
- Algorithms for Reinforcement Learning. Szepesvari et. al.
- An Introduction to Deep Reinforcement Learning. Francois-Lavet et. al.
- Deep Reinforcement Learning Hands-On. Lapan
- Deep Reinforcement Learning in Action. Zai & Brown
- Foundations of Deep Reinforcement Learning. Graesser & Keng
- Grokking Deep Reinforcement Learning. Morales
Tutorials
Competitions
Showing a sample of 212 resources. View the full list on GitHub →