Summary of Efficient Diversity-based Experience Replay For Deep Reinforcement Learning, by Kaiyan Zhao et al.
Efficient Diversity-based Experience Replay for Deep Reinforcement Learning
by Kaiyan Zhao, Yiming Wang, Yuyang Chen, Yan Li, Leong Hou U, Xiaoguang Niu
First submitted to arxiv on: 27 Oct 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 propose a novel experience replay method called Efficient Diversity-based Experience Replay (EDER) to improve learning efficiency in reinforcement learning. EDER employs a deterministic point process to model sample diversity and prioritizes replay based on this diversity. Additionally, the approach incorporates Cholesky decomposition for handling large state spaces and rejection sampling to select diverse samples. The authors conduct extensive experiments on robotic manipulation tasks, Atari games, and realistic indoor environments in Habitat, demonstrating significant improvements in learning efficiency and superior performance in high-dimensional, realistic environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to use past experiences to learn faster in reinforcement learning. They called it Efficient Diversity-based Experience Replay (EDER). EDER looks at how different the samples are from each other and chooses which ones to replay based on that. This helps it handle big state spaces and choose the most useful experiences. The team tested EDER on robots, video games, and simulated indoor environments. The results show that EDER makes learning faster and better in these types of scenarios. |
Keywords
* Artificial intelligence * Reinforcement learning
