Parametric PSRO: Towards Self-Adaptive Population-based Game Solving

Core Concepts

This work proposes a parametric version of Policy-Space Response Oracles (PPSRO) that unifies various PSRO variants, and then develops a novel self-adaptive PSRO (SPSRO) framework that can automatically determine the optimal hyperparameter values during the PSRO running.

Abstract

The paper makes three key contributions: Parametric PSRO (PPSRO): The authors introduce several hyperparameters to PSRO, including the weights of different meta-solvers (game-free hyperparameters) and the initialization and number of updates for the best response (BR) policies (game-based hyperparameters). This parametric version unifies various PSRO variants such as Gradient Descent Ascent (GDA) and different PSRO algorithms. Self-Adaptive PSRO (SPSRO): The authors cast the hyperparameter value selection of PPSRO as a hyperparameter optimization (HPO) problem, where the goal is to learn an HPO policy that can self-adaptively determine the optimal hyperparameter values during the PSRO running. Offline HPO Approach: To overcome the poor performance of online HPO methods, the authors propose a novel offline HPO approach based on the Transformer architecture. This approach formulates the HPO policy optimization as a sequence modeling problem, where a Transformer model is trained on an offline dataset and then used to predict the hyperparameter values conditioned on the past epochs of SPSRO. The experiments on various two-player zero-sum games, including both normal-form and extensive-form games, demonstrate the superiority of SPSRO with the Transformer-based HPO approach over different baselines.

Stats

The number of BR training episodes determined by Optuna and Transformer in Leduc Poker and Goofspiel.

Quotes

"By combining multiple meta-solvers, we could obtain better performance than using a single meta-solver." "Transformer-based HPO can learn a better prior distribution of hyperparameter values from offline data, providing a better scheme for weighting multiple meta-solvers, and therefore, achieving better performance than Optuna which is an online method." "The trained Transformer model can be applied to the games that are different from the training dataset, corresponding to the desiderata of a universal and plug-and-play hyperparameter value selector."

Key Insights Distilled From

Self-adaptive PSRO: Towards an Automatic Population-based Game Solver

by Pengdeng Li,... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11144.pdf

Deeper Inquiries

How can the proposed self-adaptive PSRO framework be extended to handle more complex game settings, such as multi-player games or games with imperfect information

The self-adaptive PSRO framework can be extended to handle more complex game settings by incorporating techniques specifically designed for multi-player games or games with imperfect information. For multi-player games, the framework can be modified to consider the interactions between multiple players and their strategies. This may involve developing new meta-solvers that can handle the dynamics of multi-player interactions and adjusting the BR oracle to account for the strategies of multiple opponents. Additionally, for games with imperfect information, the framework can be adapted to incorporate techniques such as partial observability and information asymmetry. This could involve developing new algorithms for estimating opponent strategies and making decisions under uncertainty.

What are the potential limitations of the Transformer-based HPO approach, and how can it be further improved to handle more challenging hyperparameter optimization problems

The Transformer-based HPO approach may have limitations in handling more challenging hyperparameter optimization problems due to the complexity and high-dimensional nature of the search space. One potential limitation is the scalability of the Transformer model to larger datasets and more complex hyperparameter configurations. Additionally, the model may struggle with capturing long-range dependencies and interactions between hyperparameters in highly nonlinear optimization problems. To address these limitations, the Transformer architecture can be further improved by incorporating techniques such as hierarchical modeling, attention mechanisms with longer context windows, and adaptive learning rates. Additionally, ensemble methods or hybrid approaches combining Transformers with other machine learning models could be explored to enhance the performance of the HPO policy.

Given the insights from this work, how can the principles of self-adaptation and automated hyperparameter tuning be applied to other areas of artificial intelligence and machine learning beyond game solving

The principles of self-adaptation and automated hyperparameter tuning demonstrated in this work can be applied to various areas of artificial intelligence and machine learning beyond game solving. For example: Neural Architecture Search (NAS): Self-adaptive techniques can be used to automatically search for optimal neural network architectures by adjusting hyperparameters related to network structure, layer configurations, and activation functions. Hyperparameter Optimization in Deep Learning: The automated HPO approach can be applied to optimize hyperparameters in deep learning models for tasks such as image classification, natural language processing, and reinforcement learning. Automated Machine Learning (AutoML): Self-adaptive methods can be integrated into AutoML pipelines to automatically tune hyperparameters, select algorithms, and optimize model performance across various datasets and tasks. Optimization Algorithms: The principles of self-adaptation can be applied to optimize optimization algorithms themselves, such as gradient descent variants, evolutionary algorithms, and metaheuristic algorithms, by automatically adjusting hyperparameters for improved convergence and performance.