在Hanabi中评估Rainbow DQN Agent与Unseen Partners
Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners
摘要：
Hanabi是一款挑战现有AI技术的合作游戏，因为它专注于对其他玩家的心理状态进行建模，以解释和预测他们的行为。虽然有代理，可以达到近乎完美的分数在游戏中商定一些共同的战略，相对较小的进展已经取得了特设合作设置，其中合作伙伴和战略是不知道的提前。在本文中，我们表明，通过自我发挥使用流行的RainbowDQN体系结构训练的代理未能与简单的rules-basedagents在训练过程中没有看到很好的合作，相反，当这些代理被训练与任何个人rules-basedagents，甚至这些代理的混合，他们未能实现良好的自我发挥分数。
英文摘要：
Hanabi is a cooperative game that challenges exist-ing AI techniques due to its focus on modeling the mental states ofother players to interpret and predict their behavior. While thereare agents that can achieve near-perfect scores in the game byagreeing on some shared strategy, comparatively little progresshas been made in ad-hoc cooperation settings, where partnersand strategies are not known in advance. In this paper, we showthat agents trained through self-play using the popular RainbowDQN architecture fail to cooperate well with simple rule-basedagents that were not seen during training and, conversely, whenthese agents are trained to play with any individual rule-basedagent, or even a mix of these agents, they fail to achieve goodself-play scores.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
