Summary of Ltl-constrained Policy Optimization with Cycle Experience Replay, by Ameesh Shah et al.
LTL-Constrained Policy Optimization with Cycle Experience Replay
by Ameesh Shah, Cameron Voloshin, Chenxi Yang, Abhinav Verma, Swarat Chaudhuri, Sanjit A. Seshia
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL)
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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 proposes Cycle Experience Replay (CyclER), a novel approach for optimizing reinforcement learning agents under Linear Temporal Logic (LTL) constraints in continuous state-action spaces. Unlike previous methods, which are limited to finite state spaces and cannot use function approximations, CyclER addresses these challenges by encouraging partial behaviors compliant with the LTL constraint. The method optimizes a scalar reward while satisfying the LTL constraints, making it suitable for tasks that require both performance and constraint satisfaction. In three continuous control domains, CyclER outperforms existing methods in finding performant and LTL-satisfying policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a robot to do certain things without getting stuck in an endless loop. The problem is that the robot doesn’t always follow the rules, so you need a way to guide it towards good behavior. This paper introduces a new approach called Cycle Experience Replay (CyclER) that helps the robot learn and follow rules while doing its tasks. CyclER is better than previous methods because it can handle complex situations where the robot has many choices to make. The researchers tested CyclER in three different scenarios and found that it was able to get the best results. |
Keywords
» Artificial intelligence » Reinforcement learning
