论文标题
向学习者学习：适应强化学习者在纸牌游戏中具有竞争力
Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Game
论文作者
论文摘要
学习如何适应复杂而动态的环境是导致我们智力的最重要因素之一。赋予人工代理商这种能力并不是一项简单的任务，尤其是在竞争性场景中。在本文中，我们介绍了一项广泛的研究，介绍了如何对流行的增强学习算法进行调整和实施，以学习和播放竞争性的多人卡游戏的现实实现。我们为学习代理人提出了特定的培训和验证程序，以评估代理商如何学会具有竞争力并解释他们如何适应彼此的比赛风格。最后，我们指出了每个代理的行为如何从他们的学习方式中得出，并为这种情况创建了一个基线，以在这种情况下进行研究。
Learning how to adapt to complex and dynamic environments is one of the most important factors that contribute to our intelligence. Endowing artificial agents with this ability is not a simple task, particularly in competitive scenarios. In this paper, we present a broad study on how popular reinforcement learning algorithms can be adapted and implemented to learn and to play a real-world implementation of a competitive multiplayer card game. We propose specific training and validation routines for the learning agents, in order to evaluate how the agents learn to be competitive and explain how they adapt to each others' playing style. Finally, we pinpoint how the behavior of each agent derives from their learning style and create a baseline for future research on this scenario.
