Summary of The Exploration-exploitation Dilemma Revisited: An Entropy Perspective, by Renye Yan et al.
The Exploration-Exploitation Dilemma Revisited: An Entropy Perspective
by Renye Yan, Yaozhong Gan, You Wu, Ling Liang, Junliang Xing, Yimao Cai, Ru Huang
First submitted to arxiv on: 19 Aug 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 paper explores the balance between exploration and exploitation in reinforcement learning, revisiting the exploration-exploitation dilemma from an entropy perspective. It presents AdaZero, an end-to-end adaptive framework that automatically determines the optimal balance of exploration and exploitation strength. Experimental results show significant performance improvements across Atari and MuJoCo environments, including a 15-fold boost in final returns on the challenging Montezuma environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding the right mix between trying new things (exploration) and sticking with what works (exploitation) in learning. The researchers discovered that entropy (a measure of uncertainty) can help us understand this balance better. They created a new way to decide when to explore or exploit, called AdaZero, which does really well on many games and simulations. |
Keywords
» Artificial intelligence » Reinforcement learning
