Summary of Elo-rated Sequence Rewards: Advancing Reinforcement Learning Models, by Qi Ju et al.
ELO-Rated Sequence Rewards: Advancing Reinforcement Learning Models
by Qi Ju, Falin Hei, Zhemei Fang, Yunfeng Luo
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 paper proposes a novel reinforcement learning algorithm called ELO-Rating based RL (ERRL) that addresses the challenges in accurately assigning rewards to state-action pairs in Long-Term RL (LTRL). The approach leverages expert preferences over trajectories to compute an ELO rating for each trajectory, which serves as its reward. A new reward redistribution algorithm is introduced to mitigate training volatility when there is no fixed anchor reward. Experimental results show that ERRL outperforms several leading baselines in long-term scenarios, demonstrating its potential for applications where conventional RL algorithms struggle. The paper also provides a thorough analysis of how expert preferences impact the outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a new way to help machines learn from mistakes by using experts’ opinions on what’s good or bad. Instead of giving rewards based on specific actions, it looks at entire paths taken by agents and uses expert ratings to decide if they’re good or not. This approach helps machines make better decisions in the long run. The authors tested their method against other popular techniques and found that it works better in complex scenarios. |
Keywords
» Artificial intelligence » Reinforcement learning
