| Name | Site | Proceedings |
|---|---|---|
| NIPS | site | proceedings |
| ICLR | site | openreview |
| ICML | site | PMLR |
| AAAI | site | proceedings, dblp |
| CORL | site | openreview |
NIPS
| Year | Papers | Proceedings |
|---|---|---|
| 2023 | papers | proceedings |
| 2022 | papers | proceedings |
| 2021 | papers | proceedings |
| 2020 | papers | proceedings |
ICLR
| Year | Papers | Proceedings |
|---|---|---|
| 2023 | papers | openreview |
| 2022 | papers | openreview |
| 2021 | papers | openreview |
| 2020 | papers | openreview |
AAAI
| Year | Papers | Proceedings |
|---|---|---|
| 2023 | papers | proceedings, dblp |
| 2022 | papers | proceedings, dblp |
| 2021 | papers | proceedings, dblp |
| 2020 | papers | proceedings, dblp |
CORL
| Year | Papers | Proceedings |
|---|---|---|
| 2023 | papers | PMLR |
| 2022 | oral, poster | PMLR |
| 2021 | program | PMLR |
| 2020 | docs | PMLR |
| Repository | Library | Note | Last Commit | Stars |
|---|---|---|---|---|
| skrl | PyTorch, JAX | modular |  |
 |
| PARL | Paddle | distributed, industry |  |
 |
| MushroomRL | PyTorch | modular |  |
 |
| coax | haiku | modular |  |
 |
| DRL with PyTorch | PyTorch | research |  |
 |
| Trax | JAX | fast |  |
 |
| CleanRL | PyTorch, JAX | single file |  |
 |
| Stable Baselines3 | PyTorch | modular |  |
 |
| Stable Baselines JAX | JAX | modular, fast |  |
 |
| PyMARL2 | JAX | multi-agent |  |
 |
| tinkoff-ai/CORL | JAX | singe file, offline RL |  |
 |
| corl-team/CORL | JAX | singe file, offline RL |  |
 |
| JaxMARL | JAX | singe file, multi-agent |  |
 |
| DI-engine | PyTorch | industry |  |
 |
| RLtools | C++ | industry, fast |  |
 |
| MiniZero | C++ | research |  |
 |
| DRLib | PyTorch | research |  |
 |
| Mava | JAX | distributed, multi-agent, fast |  |
 |
| MINIMAX | JAX | UED, auto-curricula |  |
 |
| Pearl | PyTorch | industry |  |
 |
| PureJaxRL | JAX | fast, research |  |
 |
| RL4CO | PyTorch | research, CO |  |
 |
| Rofunc | PyTorch | Robotics, research |  |
 |
| RLX | MLX | Apple silicon |  |
 |
| SheepRL | PyTorch | research, industry |  |
 |
| TorchRL | PyTorch | modular |  |
 |
| MctX | JAX | fast, research |  |
 |
| DiffRL | PyTorch | distributed, research |  |
 |
| RLLTE | PyTorch | distributed, industry |  |
 |
| Flashbax | JAX | fast, replay buffer |  |
 |
| d3rlpy | PyTorch | offline RL, modular |  |
 |
| JaxIRL | JAX | inverse RL |  |
 |
| Imitation | PyTorch | imitation learning |  |
 |
- Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
- Reinforcement-Learning-2nd-Edition-by-Sutton-Exercise-Solutions
- Book-Mathematical-Foundation-of-Reinforcement-Learning
- Learning Reinforcement Learning · Denny's Blog
- OpenAI Spinning Up
- HuggingFace Depp RL Course
- RL Course by David Silver
- RL Course by Sergey Levine
- Berkeley’s Deep RL Bootcamp
- Berkeley’s Deep RL Course
- Dive in to Deep Learning
- AI for Beginners
- Generative AI for Beginners
- ML for Beginners
- Practical Deep Learning for Coders
- Machine Learning Curriculum
- RL Tips and Tricks - Stable Baselines3
- RL Baselines3 Zoo
- Gradient Flow - Borealis AI
- How to Make Sense of the Reinforcement Learning Agents? What and Why I Log During Training and Debug - neptune.ai
- How to Manage a Deep Reinforcement Learning Research Team: Part 1 - neptune.ai
- How to Manage a Deep Reinforcement Learning Research Team: Part 2 - neptune.ai
- How to Structure, Organize, Track and Manage RL Projects - neptune.ai
- Logging in Reinforcement Learning Frameworks - neptune.ai
