Summary of Near-optimal Policy Identification in Robust Constrained Markov Decision Processes Via Epigraph Form, by Toshinori Kitamura et al.
Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form
by Toshinori Kitamura, Tadashi Kozuno, Wataru Kumagai, Kenta Hoshino, Yohei Hosoe, Kazumi Kasaura, Masashi Hamaya, Paavo Parmas, Yutaka Matsuo
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 presents a new algorithm for designing safe policies in uncertain environments, specifically in Markov decision processes (MDPs) where constraints must be satisfied. The algorithm is guaranteed to identify near-optimal policies that minimize cumulative cost while ensuring worst-case satisfaction of constraints across various environments. The conventional policy gradient approach can become stuck in suboptimal solutions due to conflicting gradients from the objective and constraint functions. To overcome this, the paper proposes a novel epigraph form for the problem, which resolves conflicts by selecting a single gradient. Building on this, the authors develop a bisection search algorithm with a policy gradient subroutine, demonstrating that it can identify an ε-optimal policy in robustly constrained MDPs with 💸(ε^-4) evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us design safer rules for uncertain situations. Usually, these rules get stuck because they have to balance many things at once. The new algorithm makes sure the rule is good enough by looking at how it does in all possible scenarios. It uses a special way of solving problems called epigraph form, which helps pick just one direction to go. Then, it finds the best rule using a search method. This can help us create better rules for situations where things might not work out as planned. |
