Summary of Advances in Preference-based Reinforcement Learning: a Review, by Youssef Abdelkareem et al.
Advances in Preference-based Reinforcement Learning: A Review
by Youssef Abdelkareem, Shady Shehata, Fakhri Karray
First submitted to arxiv on: 21 Aug 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 This survey presents a unified framework for preference-based reinforcement learning (PbRL), which utilizes human preferences as feedback to guide learning agents. PbRL has gained popularity due to its advantages over traditional RL, with many recent advances improving scalability and efficiency. The paper provides an overview of theoretical guarantees, benchmarking work, and applications in complex real-world tasks, highlighting the limitations and future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PbRL is a new way for machines to learn from humans by using their preferences as feedback instead of numbers. This helps machines do tasks better and more efficiently. The paper looks at all the recent advances in PbRL and how it can be used to solve big problems in real life. It also talks about what we know so far and where we need to go next. |
Keywords
* Artificial intelligence * Reinforcement learning
