核心概念
A-PSRO introduces the advantage function to enhance strategy learning efficiency in normal-form games.
要約
In this work, A-PSRO is proposed as a unified open-ended learning framework for both zero-sum and general-sum games. The advantage function is introduced as an evaluation metric for strategies, enabling efficient learning objectives. Experimental results show significant improvements in exploitability reduction and reward escalation compared to previous PSRO algorithms.
1. Introduction:
- Nash equilibrium modeling strategic behavior in games.
- Multiagent Reinforcement Learning (MARL) progress.
2. Notation and Background:
- Normal-form games represented by (N, A, U).
- Agents adopt strategies π over actions a ∈A.
3. Advantage Policy Space Response Oracle:
- Exploitability extended to advantage function.
- Properties of the advantage function in zero-sum games.
4. A-PSRO for Solving Zero-Sum Games:
- LookAhead module enhances convergence to Nash equilibrium.
5. A-PSRO for Solving Two-player General-Sum Games:
- Advantage function properties in simplified general-sum games.
6. Experiment Results and Discussion:
- Reduction in exploitability across various game environments.
7. Conclusion:
- A-PSRO efficiently learns equilibrium strategies in multi-agent systems.
統計
A-PSSOはゼロサムゲームでの利用可能性を示す。
A-PSSOは前のPSROアルゴリズムよりも優れた結果を達成する。