Summary of Safe Reinforcement Learning For Constrained Markov Decision Processes with Stochastic Stopping Time, by Abhijit Mazumdar and Rafal Wisniewski and Manuela L. Bujorianu
Safe Reinforcement Learning for Constrained Markov Decision Processes with Stochastic Stopping Time
by Abhijit Mazumdar, Rafal Wisniewski, Manuela L. Bujorianu
First submitted to arxiv on: 23 Mar 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 an online reinforcement learning algorithm for constrained Markov decision processes with a safety constraint. The algorithm, based on linear programming, learns an optimal policy without violating safety constraints during the learning phase. The learned policy is shown to be safe with high confidence. The paper also proposes a method to compute a safe baseline policy and demonstrates efficient exploration by defining a subset of the state-space called proxy set. The algorithm does not require a process model, making it a practical solution for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created an online learning system that helps make good decisions while keeping safety in mind. This is important because traditional systems can learn and improve without considering potential risks or consequences. The new algorithm uses linear programming to find the best decision-making strategy, which ensures that the learned policy stays safe throughout the process. |
Keywords
* Artificial intelligence * Online learning * Reinforcement learning
