Practical Large-Scale Distributed Parallel Monte-Carlo Tree Search Applied to Molecular Design
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It is common practice to use large computational resources to train neural networks, as is known from many examples, such as reinforcement learning applications. However, while massively parallel computing is often used for training models, it is rarely used for searching solutions for combinatorial optimization problems. In this paper, we propose to apply a hash function based distributed parallel Monte-Carlo Tree Search (MCTS) to a real-world problem of molecular design. By running our massively parallel MCTS combined with a simple RNN on 1024 CPU cores for 10 minutes, we achieved a score on a molecular design problem that significantly outperforms existing work. Whereas existing studies on massively scalable parallel MCTS only compare the number of rollouts, we prove the practicality of the algorithm by comparing the quality of the solutions obtained in practice. This method is generic and is expected to speed up other applications of MCTS.
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