Summary of The Limits Of Pure Exploration in Pomdps: When the Observation Entropy Is Enough, by Riccardo Zamboni et al.
The Limits of Pure Exploration in POMDPs: When the Observation Entropy is Enough
by Riccardo Zamboni, Duilio Cirino, Marcello Restelli, Mirco Mutti
First submitted to arxiv on: 18 Jun 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 pure exploration in Markov decision processes under partial observability, a problem that has received limited attention despite being crucial in applications such as finance and robotics. The authors propose a simple approach by maximizing entropy over observations instead of true latent states. They provide lower and upper bounds for the approximation of state entropy and show how knowledge of the observation function can be used to compute a regularization term to improve performance. This work provides a flexible approach to bring advances in state entropy maximization to partially observable Markov decision processes (POMDPs) and a theoretical characterization of its intrinsic limits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers tackle a big problem in artificial intelligence: how to learn from incomplete information. Imagine you’re trying to figure out what’s going on in a game or system, but you only get partial clues. That’s the challenge they’re addressing. They propose a new way of solving it by focusing on the uncertainty of the observations rather than the true underlying state. This approach could have important implications for fields like finance and robotics, where there is often incomplete information. |
Keywords
» Artificial intelligence » Attention » Regularization
