Summary of Deterministic Policies For Constrained Reinforcement Learning in Polynomial Time, by Jeremy Mcmahan
Deterministic Policies for Constrained Reinforcement Learning in Polynomial Time
by Jeremy McMahan
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS)
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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 novel algorithm efficiently computes near-optimal deterministic policies for constrained reinforcement learning (CRL) problems by combining three key ideas: value-demand augmentation, action-space approximate dynamic programming, and time-space rounding. The algorithm constitutes a fully polynomial-time approximation scheme (FPTAS) for any time-space recursive (TSR) cost criteria, including classical expectation, almost sure, and anytime constraints. This work answers three open questions in the fields of CRL and TSR, demonstrating the polynomial-time approximability of policies constrained by various criteria. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This algorithm helps computers make decisions quickly and efficiently when faced with complex problems. It uses a combination of techniques to find the best solution that meets certain conditions. The paper shows that it’s possible to find good solutions in a reasonable amount of time, even for very large problems. This is important because many real-world problems require making decisions based on incomplete information. |
Keywords
» Artificial intelligence » Reinforcement learning
