This work considers a novel information design problem and studies how the craft of payoff-relevant environmental signals solely can influence the behaviors of intelligent agents. The agents' strategic interactions are captured by an incomplete-information Markov game, in which each agent first selects one environmental signal from multiple signal sources as additional payoff-relevant information and then takes an action. There is a rational information designer (designer) who possesses one signal source and aims to control the equilibrium behaviors of the agents by designing the information structure of her signals sent to the agents. An obedient principle is established which states that it is without loss of generality to focus on the direct information design when the information design incentivizes each agent to select the signal sent by the designer, such that the design process avoids the predictions of the agents' strategic selection behaviors. We then introduce the design protocol given a goal of the designer referred to as obedient implementability (OIL) and characterize the OIL in a class of obedient perfect Bayesian Markov Nash equilibria (O-PBME). A new framework for information design is proposed based on an approach of maximizing the optimal slack variables. Finally, we formulate the designer's goal selection problem and characterize it in terms of information design by establishing a relationship between the O-PBME and the Bayesian Markov correlated equilibria, in which we build upon the revelation principle in classic information design in economics. The proposed approach can be applied to elicit desired behaviors of multi-agent systems in competing as well as cooperating settings and be extended to heterogeneous stochastic games in the complete- and the incomplete-information environments.
Convergence of Decentralized Actor-Critic Algorithm in General-sum Markov Games
Electrical Engineering and Computer Sciences (EECS) at UC Berkeley（加州大学伯克利分校电子工程与计算机科学系）；Operations Research and Information Engineering (ORIE) at Cornell University（康奈尔大学运筹学与信息工程系）
$Q$ Can Play That Game: Online Fitted $Q$-Iteration for Continuous-Action Zero-Sum Markov Games with Convex-Concave Function Approximation
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；The University of California, Berkeley（加州大学伯克利分校）；Massachusetts Institute of Technology (MIT) Lincoln Labs（麻省理工学院林肯实验室）
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games
University of California, Irvine（加州大学欧文分校）；Carnegie Mellon University（卡内基梅隆大学）；Columbia University（哥伦比亚大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）
纳什均衡策略的求解是多智能体强化学习的核心问题，在理论和实践中都受到了广泛的关注。然而，迄今为止，可证明的保证要么仅限于完全竞争或合作的情况，要么强加了在大多数实际应用中难以满足的强有力的假设。在这项工作中，我们离开这些先前的结果调查无限地平线\emph{adversarial team Markov games}，一个自然的和动机良好的一类游戏中，一队同样感兴趣的球员-在没有任何明确的协调或沟通-是竞争对手的球员。这种设置允许零和马尔可夫博弈和马尔可夫势博弈的统一处理，并作为一个步骤，以模拟更现实的战略互动，同时具有竞争和合作的利益。我们的主要贡献是第一个算法计算平稳$ε$ -近似纳什均衡的对抗团队马尔可夫游戏的计算复杂性是多项式的所有自然参数的游戏，以及$1/ε$。所提出的算法是特别自然和实用的，它是基于执行独立的策略梯度步骤，为每个球员在团队中，在串联与最佳对策从对手的一方，反过来，对手的政策，然后通过求解一个精心构造的线性规划。我们的分析利用非标准的技术来建立KKT最优性条件的非凸约束的非线性规划，从而导致自然的解释诱导拉格朗日乘子。一路上，我们显着扩展了对抗（正常形式）团队游戏中的最优策略的重要特征，由于Von Stengel和Koller（GEB '97）。
High-Probability Nash Regret for Decentralized Learning in Markov $α$-Potential Games: Episodic and Fully Online Asynchronous Algorithms with Applications to Markov Congestion Games
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
