Summary of Syllabus: Portable Curricula For Reinforcement Learning Agents, by Ryan Sullivan et al.
Syllabus: Portable Curricula for Reinforcement Learning Agents
by Ryan Sullivan, Ryan Pégoud, Ameen Ur Rahmen, Xinchen Yang, Junyun Huang, Aayush Verma, Nistha Mitra, John P. Dickerson
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper introduces Syllabus, a library for training reinforcement learning (RL) agents with curriculum learning. Despite its importance in many RL successes, none of the major libraries directly support curriculum learning or include implementations. The library provides a universal API for algorithms, popular method implementations, and infrastructure for integrating with distributed code written in various RL libraries. It simplifies designing new algorithms and applying existing ones to new environments. The paper demonstrates Syllabus’s capabilities on multiple domains and two premier challenges: NetHack and Neural MMO, achieving strong results compared to state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Syllabus is a special kind of library that helps train artificial intelligence agents better by teaching them in steps. This is important because it makes the agents smarter and more robust. Right now, there are no libraries that do this well, so the researchers created one. It’s like a toolbox for building new algorithms or using existing ones on different games or environments. The library can be used to train agents on many different types of problems and even on special kinds of games. |
Keywords
» Artificial intelligence » Curriculum learning » Reinforcement learning
