Summary of Distributionally Robust Constrained Reinforcement Learning Under Strong Duality, by Zhengfei Zhang et al.
Distributionally Robust Constrained Reinforcement Learning under Strong Duality
by Zhengfei Zhang, Kishan Panaganti, Laixi Shi, Yanan Sui, Adam Wierman, Yisong Yue
First submitted to arxiv on: 22 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 This paper investigates Distributionally Robust Constrained Reinforcement Learning (DRC-RL), a challenging problem that combines distributional robustness and constraints in RL settings. The goal is to maximize expected rewards while accounting for environmental distribution shifts and satisfying constraints motivated by safety or budget considerations. Despite advancements in individual problems, there are no existing algorithms with end-to-end convergence guarantees for DRC-RL. To address this, the authors develop an algorithmic framework based on strong duality, providing the first efficient and provable solution in a class of environmental uncertainties. The framework exposes an inherent structure in DRC-RL that prevents popular iterative methods from tractably solving the problem. Experimental results on a car racing benchmark demonstrate the effectiveness of the proposed algorithm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine playing a game where you need to adjust your strategy based on changing circumstances, like driving a car that behaves differently in different weather conditions. This paper explores how to make decisions in such situations while staying within certain limits or rules. The authors develop a new approach called Distributionally Robust Constrained Reinforcement Learning (DRC-RL) that helps agents adapt to changing environments and stay safe. They test their method on a car racing game and show it can improve performance compared to other approaches. |
Keywords
* Artificial intelligence * Reinforcement learning
