Summary of On the Role Of Information Structure in Reinforcement Learning For Partially-observable Sequential Teams and Games, by Awni Altabaa et al.
On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games
by Awni Altabaa, Zhuoran Yang
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel reinforcement learning model is proposed to explicitly represent the information structure in sequential decision-making problems. The classical models assume a simple and regular information structure, while real-world problems involve complex interdependencies between system variables. The new model is used to analyze the statistical hardness of general sequential decision-making problems, obtaining a characterization via a graph-theoretic quantity. An upper bound on the sample complexity of learning such problems is proven, which recovers known tractability results and provides a systematic way to identify new tractable classes of problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new kind of computer model that can make decisions based on information about how different things are connected in time. Right now, most models assume that these connections are simple and don’t change much over time. But real-world situations often have complex connections that change over time, making it harder to make good decisions. The new model tries to fix this by being more flexible and representing the connections between things in a better way. It then uses this model to study how hard it is to learn from these complex connections, and finds a way to prove that some types of problems can be solved more easily than others. |
Keywords
* Artificial intelligence * Reinforcement learning
