Summary of Enhancing Efficiency Of Safe Reinforcement Learning Via Sample Manipulation, by Shangding Gu et al.
Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation
by Shangding Gu, Laixi Shi, Yuhao Ding, Alois Knoll, Costas Spanos, Adam Wierman, Ming Jin
First submitted to arxiv on: 31 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 proposes a novel approach to safe reinforcement learning (RL) called Efficient Safe Policy Optimization (ESPO). The goal of ESPO is to efficiently learn a safe policy that maximizes long-term rewards while satisfying safety constraints. The approach employs an optimization framework with three modes: maximizing rewards, minimizing costs, and balancing the trade-off between the two. By dynamically adjusting the sampling process based on the observed conflict between reward and safety gradients, ESPO theoretically guarantees convergence, optimization stability, and improved sample complexity bounds. Experimental results on the Safety-MuJoCo and Omnisafe benchmarks demonstrate that ESPO outperforms existing baselines in terms of reward maximization and constraint satisfaction, while also achieving substantial gains in sample efficiency, requiring 25–29% fewer samples than baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure artificial intelligence (AI) agents work safely. The goal is to teach AI agents how to make good choices that don’t hurt people or the environment. The researchers came up with a new way to do this called Efficient Safe Policy Optimization (ESPO). ESPO helps AI agents learn faster and make better decisions while following safety rules. Tests showed that ESPO works really well, doing better than other approaches in some cases. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
