Summary of Efficient Exploration in Average-reward Constrained Reinforcement Learning: Achieving Near-optimal Regret with Posterior Sampling, by Danil Provodin et al.
Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling
by Danil Provodin, Maurits Kaptein, Mykola Pechenizkiy
First submitted to arxiv on: 29 May 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 algorithm, based on posterior sampling, demonstrates significant improvements in learning Constrained Markov Decision Processes (CMDP) with infinite-horizon undiscounted settings. By achieving near-optimal regret bounds and outperforming existing methods empirically, this novel approach shows promising results for constrained reinforcement learning. Theoretical analyses provide a Bayesian regret bound of (DS) for communicating CMDPs with S states, A actions, and diameter D, matching the lower bound in terms of time horizon T. This computationally tractable algorithm showcases its efficacy by outperforming existing algorithms in empirical evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to learn in situations where there are constraints. It uses something called posterior sampling to make decisions. The method is really good at making choices that balance different costs and rewards. The researchers proved that their approach is one of the best, with a regret bound of (DS). They also showed that it works better than other methods in practice. |
Keywords
» Artificial intelligence » Reinforcement learning
