Summary of Surprise-adaptive Intrinsic Motivation For Unsupervised Reinforcement Learning, by Adriana Hugessen et al.
Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement Learning
by Adriana Hugessen, Roger Creus Castanyer, Faisal Mohamed, Glen Berseth
First submitted to arxiv on: 27 May 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 proposes a novel approach to unsupervised reinforcement learning (RL) by introducing an agent that can adapt its objective online, depending on the entropy conditions of the environment. The authors explore both entropy-minimizing and entropy-maximizing objectives, which have been shown to be effective in different environments. However, they find that neither method alone results in an agent that consistently learns intelligent behavior across environments. To address this issue, they frame the choice as a multi-armed bandit problem and devise a novel intrinsic feedback signal that captures the agent’s ability to control entropy. The authors demonstrate that such agents can learn to control entropy and exhibit emergent behaviors in both high- and low-entropy regimes, and can learn skillful behaviors in benchmark tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching computers to make smart choices without being told what to do. It shows that two different ways of doing this – one that tries to find the simplest solution and another that encourages exploration – work well in certain situations. But when it comes to learning new skills, neither way is good enough on its own. To fix this, the researchers came up with a new approach that lets the computer adapt its strategy based on what’s happening in the environment. They tested their idea and found that it worked well in different kinds of environments and helped computers learn new skills. |
Keywords
* Artificial intelligence * Reinforcement learning * Unsupervised
