Summary of Minimizing Weighted Counterfactual Regret with Optimistic Online Mirror Descent, by Hang Xu et al.
Minimizing Weighted Counterfactual Regret with Optimistic Online Mirror Descent
by Hang Xu, Kai Li, Bingyun Liu, Haobo Fu, Qiang Fu, Junliang Xing, Jian Cheng
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
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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 introduces a novel Counterfactual Regret Minimization (CFR) algorithm, PDCFR+, which combines the strengths of PCFR+ and Discounted CFR (DCFR). By minimizing weighted counterfactual regret with optimistic Online Mirror Descent (OMD), PDCFR+ mitigates the negative effects of dominated actions and accelerates convergence. Theoretical analyses show that PDCFR+ converges to a Nash equilibrium under different weighting schemes, while experimental results demonstrate its fast convergence in common imperfect-information games. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PDCFR+ is a new way to play imperfect-information games better. It uses a combination of two old ideas, PCFR+ and DCFR, to make decisions faster and more accurate. This helps by ignoring bad options and using predictions to speed up the process. The paper shows that PDCFR+ works well in many situations and is better than other methods. |
