Summary of Pufferlib: Making Reinforcement Learning Libraries and Environments Play Nice, by Joseph Suarez
PufferLib: Making Reinforcement Learning Libraries and Environments Play Nice
by Joseph Suarez
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The PufferLib library is designed to facilitate the integration of reinforcement learning models with various environments. The library provides one-line wrappers for environments and fast vectorization to accelerate training. With PufferLib, users can leverage familiar libraries like CleanRL and SB3 to train models on a range of benchmarks, from classic games like Atari and Procgen to complex simulators like NetHack and Neural MMO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PufferLib makes it easier to work with different environments and reinforcement learning models by providing one-line wrappers that eliminate compatibility problems. This means you can use familiar libraries and train your models on a variety of tasks, from simple games to complex simulations. The library also includes tools for fast vectorization, which helps speed up training. |
Keywords
* Artificial intelligence * Reinforcement learning * Vectorization
