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Parallelization of Monte Carlo Tree Search in Continuous Domains

By Karl Kurzer, Christoph Hörtnagl and J. Marius Zöllner


Monte Carlo Tree Search (MCTS) has proven to be capable of solving challenging tasks in domains such as Go, chess and Atari. Previous research has developed parallel versions of MCTS, exploiting today's multiprocessing architectures. These studies focused on versions of MCTS for the discrete case. Our work builds upon existing parallelization strategies and extends them to continuous domains. In particular, leaf parallelization and root parallelization are studied and two final selection strategies that are required to handle continuous states in root parallelization are proposed. The evaluation of the resulting parallelized continuous MCTS is conducted using a challenging cooperative multi-agent system trajectory planning task in the domain of automated vehicles

Topics: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Year: 2020
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