Traditionally, MCTS was usually used in computer programs playing turn-based-strategy games, such as Go, chess, shogi, and poker.
Monte Carlo Tree Search (MCTS) (Browne et al., Citation2012) is one of the most well-known advanced search methods, which has shown great performance in several applications domains (Chaslot et al., Citation2006). However, the recent development of advanced search methods has made search-based approaches a potentially promising direction for workflow scheduling. Search based approaches were less considered in the previous workflow scheduling studies because of the search space explosion problem, especially when dealing with large scale workflows and parallel computing platforms. genetic algorithm (Srinivas & Patnaik, Citation1994) and other evolutionary algorithms. Another well-known category of workflow scheduling approaches is based on meta-heuristic methods (Keshanchi et al., Citation2017 Xu et al., Citation2014), e.g. Most of them are heuristic-based algorithms like HEFT (Topcuoglu et al., Citation2002), PEFT (Arabnejad & Barbosa, Citation2014), and IPPTS (Djigal et al., Citation2021). There are a lot of methods proposed to resolve the challenging NP-complete workflow scheduling problem in the literature. To improve computational performance, workflow scheduling (Topcuoglu et al., Citation2002) has become an important research topic on modern parallel processing platforms, including various high-performance computing platforms and cloud computing environments. We provide regret bounds for both algorithms and demonstrate their empirical effectiveness in several high-dimensional problems including two difficult robotics planning problems.As the computational structure of modern scientific and engineering applications grows ever more complex and large-scale, workflow computing has become an inevitable parallel processing model. The voot algorithm has an instance of voo at each node in the tree. The voo algorithm uses Voronoi partitioning to guide sampling, and is particularly efficient in high-dimensional spaces. We provide a novel MCTS algorithm (voot) for deterministic environments with continuous action spaces, which, in turn, is based on a novel black-box function-optimization algorithm (voo) to efficiently sample actions. Monte Carlo tree search (MCTS) is an effective strategy for planning in discrete action spaces. Many important applications, including robotics, data-center management, and process control, require planning action sequences in domains with continuous state and action spaces and discontinuous objective functions.
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