Summary of Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks, By Shuai Han et al.
Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks
by Shuai Han, Mehdi Dastani, Shihan Wang
First submitted to arxiv on: 25 Jan 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 The proposed RL algorithm aims to improve sample efficiency by automatically structuring reward functions for complex tasks, given minimal knowledge about the task in the form of labels signifying subtasks. The algorithm trains a high-level policy that selects optimal sub-tasks and a low-level policy that efficiently completes each sub-task. Compared to state-of-the-art baselines, the approach shows significant performance gains as task difficulty increases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research paper is about making machines learn more effectively in situations where they don’t get rewards often. Right now, people have to design these reward systems by hand or use complicated methods that can’t handle complex tasks. But this new algorithm lets the machine figure out how to structure its own rewards based on some basic information about what it should be doing. This makes the learning process much more efficient and accurate. |
