Summary of Efficient Learning Of Pomdps with Known Observation Model in Average-reward Setting, by Alessio Russo et al.
Efficient Learning of POMDPs with Known Observation Model in Average-Reward Setting
by Alessio Russo, Alberto Maria Metelli, Marcello Restelli
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 abstract proposes a novel approach for dealing with Partially Observable Markov Decision Processes (POMDPs) in an average-reward infinite-horizon setting with an unknown transition model. The Observation-Aware Spectral (OAS) estimation technique is introduced, which learns POMDP parameters from samples collected using a belief-based policy. The OAS-UCRL algorithm balances exploration-exploitation using the optimism in the face of uncertainty principle, combining episodes of increasing length. The algorithm’s consistency and regret guarantee are proven, showing efficient scaling with state, action, and observation space dimensionality. Numerical simulations validate and compare the proposed technique to baseline approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary POMDPs can be tricky to work with! This paper proposes a new way to deal with them by learning from samples collected using a special kind of policy. They introduce a method called OAS-UCRL, which balances trying new things (exploration) and sticking with what works best (exploitation). The algorithm gets better as it goes along, and it can handle big state and observation spaces. The authors tested their approach and showed that it’s effective and efficient. |
