论文标题
基于优先级的同步，用于更快的游戏学习速度
Priority Based Synchronization for Faster Learning in Games
论文作者
论文摘要
游戏中的学习已被广泛用于解决许多合作的多代理问题，例如覆盖范围控制，共识，自我调查或车辆目标分配。该领域中的一种标准方法是将问题提出为潜在的游戏，并使用算法（例如日志线性学习）来实现全球最佳配置的随机稳定性。此类学习算法的标准版本是异步的，即只有一个代理在每轮学习过程中更新其动作。为了实现更快的学习，我们提出了一种基于分散的代理的随机优先级的同步策略，这允许多个代理在不影响彼此的效用或可行的动作时同时更改其行动。我们表明，建议的方法可以集成到任何标准的异步学习算法中，以提高收敛速度，同时保持限制行为（例如随机稳定的配置）。在覆盖范围控制方案中，我们通过模拟来支持我们的理论结果。
Learning in games has been widely used to solve many cooperative multi-agent problems such as coverage control, consensus, self-reconfiguration or vehicle-target assignment. One standard approach in this domain is to formulate the problem as a potential game and to use an algorithm such as log-linear learning to achieve the stochastic stability of globally optimal configurations. Standard versions of such learning algorithms are asynchronous, i.e., only one agent updates its action at each round of the learning process. To enable faster learning, we propose a synchronization strategy based on decentralized random prioritization of agents, which allows multiple agents to change their actions simultaneously when they do not affect each other's utility or feasible actions. We show that the proposed approach can be integrated into any standard asynchronous learning algorithm to improve the convergence speed while maintaining the limiting behavior (e.g., stochastically stable configurations). We support our theoretical results with simulations in a coverage control scenario.
