Summary of Feasibility Consistent Representation Learning For Safe Reinforcement Learning, by Zhepeng Cen et al.
Feasibility Consistent Representation Learning for Safe Reinforcement Learning
by Zhepeng Cen, Yihang Yao, Zuxin Liu, Ding Zhao
First submitted to arxiv on: 20 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 A novel framework for safe reinforcement learning called Feasibility Consistent Safe Reinforcement Learning (FCSRL) is proposed to balance safety constraints with reward optimization. The framework combines representation learning with feasibility-oriented objectives to extract safety-related information from state representations, leveraging self-supervised learning and a more learnable safety metric. Compared to previous baselines, FCSRL achieves superior performance in vector-state and image-based tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new approach for safe reinforcement learning that helps machines make good decisions without harming people or the environment. They create a way to teach machines to understand what is safe and what is not by using representation learning, which is a type of machine learning. This method works better than other methods in different scenarios. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Reinforcement learning » Representation learning » Self supervised
