Summary of Self-adaptive Psro: Towards An Automatic Population-based Game Solver, by Pengdeng Li et al.
Self-adaptive PSRO: Towards an Automatic Population-based Game Solver
by Pengdeng Li, Shuxin Li, Chang Yang, Xinrun Wang, Xiao Huang, Hau Chan, Bo An
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA)
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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 Policy-Space Response Oracles (PSRO) as a general framework for learning equilibrium policies in two-player zero-sum games. While PSRO has achieved state-of-the-art performance, existing works often rely on hand-crafted hyperparameter value selection, which requires extensive domain knowledge and limits the applicability of PSRO to different games. The authors aim to develop self-adaptive methods for determining optimal hyperparameters, proposing a parametric PSRO that unifies gradient descent ascent (GDA) and PSRO variants. They also introduce the self-adaptive PSRO (SPSRO), which learns an optimization policy using the Transformer architecture to optimize hyperparameter values during PSRO runs. The authors demonstrate the superiority of SPSRO over baselines in various two-player zero-sum games. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help computers play games better. It’s called Policy-Space Response Oracles (PSRO), and it already does really well at learning how to play certain games. But right now, people have to choose the best settings for PSRO by hand, which can be tricky and only works well for specific types of games. The authors are trying to figure out a way to let the computer decide what settings work best on its own. They came up with two new ideas: a way to combine different PSRO methods together, and an approach that uses a special kind of AI model called Transformer to help make decisions about which settings to use. By testing these ideas on different games, they showed that their new approach works better than the old ways. |
Keywords
» Artificial intelligence » Gradient descent » Hyperparameter » Optimization » Transformer
