Using Graph Convolutional Networks and TD($\lambda$) to play the game of Risk

• URL: http://arxiv.org/abs/2009.06355v1
• Date: Thu, 10 Sep 2020 18:47:08 GMT
• Title: Using Graph Convolutional Networks and TD($\lambda$) to play the game of Risk
• Authors: Jamie Carr
• Abstract summary: Risk is a 6 player game with significant randomness and a large game-tree complexity. Previous AIs focus on creating high-level handcrafted features determine agent decision making. I create D.A.D, A Risk agent using temporal difference reinforcement learning to train a Deep Neural Network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Risk is 6 player game with significant randomness and a large game-tree complexity which poses a challenge to creating an agent to play the game effectively. Previous AIs focus on creating high-level handcrafted features determine agent decision making. In this project, I create D.A.D, A Risk agent using temporal difference reinforcement learning to train a Deep Neural Network including a Graph Convolutional Network to evaluate player positions. This is used in a game-tree to select optimal moves. This allows minimal handcrafting of knowledge into the AI, assuring input features are as low-level as possible to allow the network to extract useful and sophisticated features itself, even with the network starting from a random initialisation. I also tackle the issue of non-determinism in Risk by introducing a new method of interpreting attack moves necessary for the search. The result is an AI which wins 35% of the time versus 5 of best inbuilt AIs in Lux Delux, a Risk variant.

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