Single-Agent Optimization Through Policy Iteration Using Monte-Carlo Tree Search
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The combination of Monte-Carlo Tree Search (MCTS) and deep reinforcement learning is state-of-the-art in two-player perfect-information games. In this paper, we describe a search algorithm that uses a variant of MCTS which we enhanced by 1) a novel action value normalization mechanism for games with potentially unbounded rewards (which is the case in many optimization problems), 2) defining a virtual loss function that enables effective search parallelization, and 3) a policy network, trained by generations of self-play, to guide the search. We gauge the effectiveness of our method in "SameGame"—a popular single-player test domain. Our experimental results indicate that our method outperforms baseline algorithms on several board sizes. Additionally, it is competitive with state-of-the-art search algorithms on a public set of positions.
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