Summary of Constrained Reinforcement Learning Under Model Mismatch, by Zhongchang Sun et al.
Constrained Reinforcement Learning Under Model Mismatch
by Zhongchang Sun, Sihong He, Fei Miao, Shaofeng Zou
First submitted to arxiv on: 2 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 This paper addresses a critical challenge in constrained reinforcement learning (RL) where models trained to optimize rewards may not generalize well to real-world environments due to model mismatch. The authors formulate this problem as constrained RL under model uncertainty, aiming to learn policies that balance reward optimization and constraint satisfaction. They propose the Robust Constrained Policy Optimization (RCPO) algorithm, which can handle large or continuous state spaces and provides theoretical guarantees on worst-case reward improvement and constraint violation at each iteration. The effectiveness of RCPO is demonstrated through experiments on various RL tasks with constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn how to make smart choices in real-life situations by developing a new way to teach computers. Right now, computers are great at playing games or doing things they were taught to do, but sometimes they forget what they learned and don’t follow the rules. To fix this, the researchers created an algorithm that can handle big state spaces (like lots of possible combinations) and guarantees good results. They tested it on several tasks with rules and showed it works well. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
