Summary of Rice: Breaking Through the Training Bottlenecks Of Reinforcement Learning with Explanation, by Zelei Cheng et al.
RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation
by Zelei Cheng, Xian Wu, Jiahao Yu, Sabrina Yang, Gang Wang, Xinyu Xing
First submitted to arxiv on: 5 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 an innovative refining scheme for deep reinforcement learning (DRL) called RICE, which incorporates explanation methods to break through training bottlenecks. The goal is to construct a new initial state distribution that combines default and critical states identified through explanation methods, encouraging the agent to explore from mixed initial states. This approach theoretically guarantees a tighter sub-optimality bound. The authors evaluate RICE in popular RL environments and real-world applications, showing significant performance enhancements over existing refining schemes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RICE is a new way to help deep reinforcement learning (DRL) agents learn better. Right now, DRL is used in many real-world applications, but it’s hard to get the best results. The authors of this paper want to change that by introducing RICE, which uses special methods to explain what’s happening during training. This helps the agent explore new things and avoid getting stuck. They tested RICE on some popular games and real-life situations, and it did really well. |
Keywords
» Artificial intelligence » Reinforcement learning
