用跨维神经网络演奏卡坦舞曲
Playing Catan with Cross-dimensional Neural Network
摘要：
卡坦是一个战略棋盘游戏，具有有趣的特性，包括多玩家，不完全信息，随机，复杂的状态空间结构（六边形棋盘，每个顶点，边和面都有自己的特征，每个玩家的卡片等），以及大的行动空间（包括谈判）。因此，在没有领域知识和经验的情况下，利用强化学习（Reinforcement Learning，简称RL）构建人工智能智能体是一个挑战。在本文中，我们引入了跨维神经网络来处理混合的信息源和各种各样的输出，并实证表明，网络显着提高了强化学习卡坦。我们还表明，第一次，RL代理可以优于jsettler，最好的启发式代理。
英文摘要：
Catan is a strategic board game having interesting properties, including multi-player, imperfect information, stochastic, complex state space structure (hexagonal board where each vertex, edge and face has its own features, cards for each player, etc), and a large action space (including negotiation). Therefore, it is challenging to build AI agents by Reinforcement Learning (RL for short), without domain knowledge nor heuristics. In this paper, we introduce cross-dimensional neural networks to handle a mixture of information sources and a wide variety of outputs, and empirically demonstrate that the network dramatically improves RL in Catan. We also show that, for the first time, a RL agent can outperform jsettler, the best heuristic agent available.
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。
