Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design

Understand

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 a novel massively parallel Monte-Carlo Tree Search (MP-MCTS) algorithm that works efficiently for 1,000 worker scale, and apply it to molecular design.
• This is the first work that applies distributed MCTS to a real-world and non-game problem.