Summary of Balance Reward and Safety Optimization For Safe Reinforcement Learning: a Perspective Of Gradient Manipulation, by Shangding Gu et al.
Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulation
by Shangding Gu, Bilgehan Sel, Yuhao Ding, Lu Wang, Qingwei Lin, Ming Jin, Alois Knoll
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research addresses the critical challenge of ensuring the safety of Reinforcement Learning (RL) in real-world applications. The authors focus on managing the trade-off between reward and safety during exploration, where improving reward performance through policy adjustments may negatively impact safety performance. To tackle this conflict, they leverage gradient manipulation theory to analyze the relationship between reward and safety gradients, proposing a soft switching policy optimization method that balances reward and safety optimization. The paper provides convergence analysis for this approach and develops a Safety-MuJoCo Benchmark to evaluate safe RL algorithms. Experimental results demonstrate that the proposed methods outperform state-of-the-art baselines in balancing reward and safety optimization. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to make Reinforcement Learning (RL) safer for real-world use. Right now, RL is great at getting rewards, but it’s not good at making sure we don’t harm people or the environment. The researchers want to fix this problem by finding a way to balance how well the AI does and how safe it is. They look at why the reward and safety goals are often in conflict and come up with a new way to optimize policies that keeps both goals in mind. To test their idea, they create a special benchmark to see how different RL algorithms do, and they find that their approach works better than others. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning