Playing Catan with Cross-dimensional Neural Network

• URL: http://arxiv.org/abs/2008.07079v1
• Date: Mon, 17 Aug 2020 04:09:29 GMT
• Title: Playing Catan with Cross-dimensional Neural Network
• Authors: Quentin Gendre, Tomoyuki Kaneko
• Abstract summary: It is challenging to build AI agents by Reinforcement Learning (RL for short) without domain knowledge nors. 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 agent available.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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