
Correlated Equilibria in Wireless Power Control Games
In this paper, we consider the problem of wireless power control in an i...
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Continuous Blackjack: Equilibrium, Deviation and Adaptive Strategy
We introduce a variant of the classic poker game blackjack – the continu...
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Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games
Finding approximate Nash equilibria in zerosum imperfectinformation ga...
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Learning When to Take Advice: A Statistical Test for Achieving A Correlated Equilibrium
We study a multiagent learning problem where agents can either learn via...
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DREAM: Deep Regret minimization with Advantage baselines and Modelfree learning
We introduce DREAM, a deep reinforcement learning algorithm that finds o...
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Computational Performance of Deep Reinforcement Learning to find Nash Equilibria
We test the performance of deep deterministic policy gradient (DDPG), a ...
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Blockchain Governance: An Overview and Prediction of Optimal Strategies using Nash Equilibrium
Blockchain governance is a subject of ongoing research and an interdisci...
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Achieving Correlated Equilibrium by Studying Opponent's Behavior Through PolicyBased Deep Reinforcement Learning
Game theory is a very profound study on distributed decisionmaking 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 method can achieve the optimal correlated equilibrium and outside the convex hull of the Nash equilibrium with limited interaction among players.
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