论文标题
蒙特卡洛树在连续域中搜索的并行化
Parallelization of Monte Carlo Tree Search in Continuous Domains
论文作者
论文摘要
事实证明，蒙特卡洛树搜索（MCT）能够解决GO，Chess和Atari等领域中具有挑战性的任务。先前的研究开发了MCT的并行版本，利用了当今的多处理体系结构。这些研究集中在离散案例的MCT版本上。我们的工作以现有的并行化策略为基础，并将其扩展到连续的域。特别地，研究了叶子并行化和根平行化，并提出了在根平行化中处理连续状态所需的两个最终选择策略。对所得的连续MCT的评估是使用自动车辆域中的具有挑战性的合作多代理系统轨迹计划任务进行的。
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.
