Summary of Beyond Optimism: Exploration with Partially Observable Rewards, by Simone Parisi et al.
Beyond Optimism: Exploration With Partially Observable Rewards
by Simone Parisi, Alireza Kazemipour, Michael Bowling
First submitted to arxiv on: 20 Jun 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 The proposed paper presents a novel exploration strategy for reinforcement learning (RL) to overcome the limitations of existing methods when rewards are not always observable. The strategy, which guarantees convergence to an optimal policy, is tested in tabular environments with and without unobservable rewards, showing improved performance compared to existing algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve exploration in RL by developing a new approach that can handle situations where rewards are sparse or unobservable. The proposed method overcomes the limitations of optimism-based methods, which can lead to suboptimal behavior when uncertainty is high. The method is tested on various benchmarking environments and shows promising results. |
Keywords
» Artificial intelligence » Reinforcement learning
