Summary of Highly Efficient Self-adaptive Reward Shaping For Reinforcement Learning, by Haozhe Ma et al.
Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learning
by Haozhe Ma, Zhengding Luo, Thanh Vinh Vo, Kuankuan Sima, Tze-Yun Leong
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduce a new approach to reinforcement learning called self-adaptive reward shaping. This technique helps agents learn more efficiently by providing more frequent and informative rewards. The method uses historical experiences to derive shaped rewards that initially encourage exploration but eventually promote exploitation. To make this efficient, the authors combine Kernel Density Estimation and Random Fourier Features to sample from Beta distributions, which adapt to the data as it accumulates. This approach is tested on various tasks with sparse rewards, showing improved performance over existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reward shaping in reinforcement learning helps agents learn faster by giving them more feedback. A new technique called self-adaptive reward shaping does just that. It uses past experiences to create rewards that are initially helpful for trying out new things but then become better at helping the agent focus on what works best. This approach is smart and efficient, using special math tools to figure out how to do this without needing a lot of data or complex calculations. The researchers tested it on different problems with very little reward, and it did really well. |
Keywords
» Artificial intelligence » Density estimation » Reinforcement learning
