Summary of Element: Episodic and Lifelong Exploration Via Maximum Entropy, by Hongming Li et al.
ELEMENT: Episodic and Lifelong Exploration via Maximum Entropy
by Hongming Li, Shujian Yu, Bin Liu, Jose C. Principe
First submitted to arxiv on: 5 Dec 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 This paper presents a novel framework called Episodic and Lifelong Exploration via Maximum ENTropy (ELEMENT), which enables exploration of environments without extrinsic rewards and transfers learned skills to downstream tasks. The proposed multiscale entropy optimization addresses the issue of vanishing rewards and computational expense. Additionally, the authors introduce an intrinsic reward for episodic entropy maximization, named average episodic state entropy, providing a theoretical upper bound. A k-nearest neighbors (kNN) graph is also proposed to speed up lifelong entropy maximization. The ELEMENT framework outperforms state-of-the-art intrinsic rewards in both episodic and lifelong settings, and has potential applications in task-agnostic pre-training and offline reinforcement learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ELEMENT helps robots learn new skills without being told what’s good or bad. It works by trying different actions to see what happens, then uses that information to decide what to do next. The approach is flexible and can be used for many tasks, such as training a robot arm to pick up small objects. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
