Summary of Achieving Tractable Minimax Optimal Regret in Average Reward Mdps, by Victor Boone et al.
Achieving Tractable Minimax Optimal Regret in Average Reward MDPs
by Victor Boone, Zihan Zhang
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 presents a new algorithm for learning average-reward Markov Decision Processes (MDPs) with minimax optimal regret guarantees, outperforming existing methods that either suffer from sub-optimal regret or computational inefficiencies. The algorithm relies on the Projected Mitigated Extended Value Iteration (PMEVI) subroutine to compute bias-constrained optimal policies efficiently. This novel approach enables computation without prior information on the optimal bias function’s span. The algorithm achieves a regret bound of (), where S A is the size of the state-action space and T is the number of learning steps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops an efficient algorithm for learning average-reward MDPs with guaranteed regret. This is important because existing methods either don’t perform well or take a long time to learn. The new algorithm uses a special technique called PMEVI, which helps it find good policies quickly and without needing to know some extra information about the problem. This approach can be used to improve other algorithms too! |
