Summary of Efficiently Training Neural Networks For Imperfect Information Games by Sampling Information Sets, By Timo Bertram et al.
Efficiently Training Neural Networks for Imperfect Information Games by Sampling Information Sets
by Timo Bertram, Johannes Fürnkranz, Martin Müller
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
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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 proposes an approach to evaluate game states in imperfect information games by estimating the value of each state as a combination of all possible consistent states. The goal is to learn a function mapping imperfect game information states to their expected values, which can be used to improve decision-making in games like Reconnaissance Blind Chess. However, constructing a perfect training set is often infeasible due to the vast number of possible states. To address this challenge, the authors empirically investigate how to distribute a budget of perfect information game evaluations among training samples to maximize the return. The results show that sampling a small number of states with high-quality target values is more effective than repeatedly sampling a larger quantity of states. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In imperfect information games, players don’t have all the facts. To make good decisions, they need to estimate the value of each game state. This paper shows how to do this by combining all possible states that are consistent with the current information. But gathering data for every possible state is hard. The authors tested different ways to gather data and found that sampling a small number of high-quality states is better than sampling many low-quality states. |
