Learning optimal strategies for line-of-sight based games

Richard Tsai
University of Texas at Austin

We present a few non-cooperative games that involve the line-of-sight of the players. The games are set in non-simply connected domains, i.e., domains that have obstacles blocking the lines-of-sights. We present Monte-Carlo-Tree Search (MCTS) based algorithms for optimally playing the games. The search algorithms are aided by deep learning and local numerical computation of optimal strategies for the players.

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