Summary of Vmfer: Von Mises-fisher Experience Resampling Based on Uncertainty Of Gradient Directions For Policy Improvement, by Yiwen Zhu et al.
vMFER: Von Mises-Fisher Experience Resampling Based on Uncertainty of Gradient Directions for Policy Improvement
by Yiwen Zhu, Jinyi Liu, Wenya Wei, Qianyi Fu, Yujing Hu, Zhou Fang, Bo An, Jianye Hao, Tangjie Lv, Changjie Fan
First submitted to arxiv on: 14 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 In this paper, researchers investigate how to improve the efficiency of policy improvement in Reinforcement Learning (RL) when using multiple critics, also known as ensemble critics. The authors focus on understanding the impact of gradient disagreements caused by these critics on policy improvement and introduce a novel method called von Mises-Fisher Experience Resampling (vMFER). This approach optimizes the policy improvement process by resampling transitions and assigning higher confidence to those with lower uncertainty of gradient directions. Experimental results show that vMFER outperforms the benchmark and is particularly well-suited for ensemble structures in RL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how to make Reinforcement Learning (RL) better at making decisions. It looks at a problem where many critics, or helpers, are used to help the decision-maker learn from its mistakes. The authors want to know how these different critics affect the learning process and find that some transitions, or steps, in the process are more reliable than others. They develop a new method called vMFER that helps the learning process by focusing on the most reliable transitions. This new approach works well when many critics are used and can help RL make better decisions. |
Keywords
» Artificial intelligence » Reinforcement learning
