Summary of Flipping-based Policy For Chance-constrained Markov Decision Processes, by Xun Shen et al.
Flipping-based Policy for Chance-Constrained Markov Decision Processes
by Xun Shen, Shuo Jiang, Akifumi Wachi, Kaumune Hashimoto, Sebastien Gros
First submitted to arxiv on: 9 Oct 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 The proposed “flipping-based policy” is a novel approach for Chance-Constrained Markov Decision Processes (CCMDPs) in safe reinforcement learning, which enables incorporating safety under uncertainties. The policy selects the next action by flipping a coin between two candidates, with probabilities varying depending on the state. This paper establishes a Bellman equation for CCMDPs and proves the existence of a flipping-based policy within optimal solution sets. Additionally, it shows that joint chance constraints can be approximated into Expected Cumulative Safety Constraints (ECSCs), allowing adaptation to constrained MDPs. The framework is demonstrated on Safety Gym benchmarks, improving the performance of existing safe RL algorithms while maintaining safety constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way for computers to make decisions safely in uncertain situations. It’s like flipping a coin to decide what to do next, but the probability of each option changes depending on the current situation. The method is called “flipping-based policy” and it can be used to improve existing ways of training computers to make safe decisions. The paper also shows that this approach can be adapted to other methods for safe decision-making. |
Keywords
» Artificial intelligence » Probability » Reinforcement learning
