Summary of Constraint-adaptive Policy Switching For Offline Safe Reinforcement Learning, by Yassine Chemingui et al.
Constraint-Adaptive Policy Switching for Offline Safe Reinforcement Learning
by Yassine Chemingui, Aryan Deshwal, Honghao Wei, Alan Fern, Janardhan Rao Doppa
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Offline safe reinforcement learning (OSRL) tackles the challenge of learning a decision-making policy from a fixed batch of training data while satisfying pre-defined safety constraints. However, adapting to varying safety constraints during deployment without retraining remains an understudied problem. To address this issue, we introduce constraint-adaptive policy switching (CAPS), a wrapper framework that builds upon existing offline RL algorithms. CAPS learns multiple policies with shared representations that optimize different reward and cost trade-offs using offline data. At test time, CAPS selects the optimal policy based on future rewards while ensuring safety constraints are met. Our experiments on 38 tasks from the DSRL benchmark demonstrate that CAPS consistently outperforms existing methods, establishing a strong baseline for OSRL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re playing a game where you need to make decisions to get rewards. But sometimes, you need to follow certain rules or “safety constraints” to avoid making mistakes. This is called offline safe reinforcement learning (OSRL). The problem is that if the rules change during the game, you might not be able to adapt without relearning everything. To solve this challenge, researchers created a new way of playing the game, called CAPS (constraint-adaptive policy switching). It’s like having multiple strategies for different situations and choosing the best one based on how likely you are to get rewards while following the rules. In tests with 38 different scenarios, CAPS performed better than previous methods, making it a strong approach for OSRL. |
Keywords
* Artificial intelligence * Reinforcement learning
