Achieving Correlated Equilibrium By Studying Opponent's Behavior Through Policy-based Deep Reinforcement Learning

Abstract

Game theory is a very profound study on distributed decision-making behavior and has been extensively developed by many scholars. However, many existing works rely on certain strict assumptions such as knowing the opponent's private behaviors, which might not be practical. In this work, we focused on two Nobel winning concepts, the Nash equilibrium and the correlated equilibrium. Specifically, we successfully reached the correlated equilibrium outside the convex hull of the Nash equilibria with our proposed deep reinforcement learning algorithm. With the correlated equilibrium probability distribution, we also propose a mathematical model to inverse the calculation of the correlated equilibrium probability distribution to estimate the opponent's payoff vector. With those payoffs, deep reinforcement learning learns why and how the rational opponent plays, instead of just learning the regions for corresponding strategies and actions. Through simulations, we showed that our proposed metho

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keytsai2020achieving

Related papers

• A Unified Perspective On Deep Equilibrium Finding (2022)0.00
• Resolving Implicit Coordination In Multi-agent Deep Reinforcement Learning With Deep Q-networks & Game Theory (2020)0.00
• Simple Uncoupled No-regret Learning Dynamics For Extensive-form Correlated Equilibrium (2021)6.34
• Combining Tree-search, Generative Models, And Nash Bargaining Concepts In Game-theoretic Reinforcement Learning (2023)0.00
• Efficient Competitive Self-play Policy Optimization (2020)0.00
• Efficiently Computing Nash Equilibria In Adversarial Team Markov Games (2022)0.00
• Imitation Learning Of Correlated Policies In Stackelberg Games (2025)0.00
• A Deep Reinforcement Learning Approach For Finding Non-exploitable Strategies In Two-player Atari Games (2022)0.00