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Monte Carlo Game Solver
We present a general algorithm to order moves so as to speedup exact gam...
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Adapting Improved Upper Confidence Bounds for Monte-Carlo Tree Search
The UCT algorithm, which combines the UCB algorithm and Monte-Carlo Tree...
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Feature selection as Monte-Carlo Search in Growing Single Rooted Directed Acyclic Graph by Best Leaf Identification
Monte Carlo tree search (MCTS) has received considerable interest due to...
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Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning
Standard planners for sequential decision making (including Monte Carlo ...
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Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions
A plethora of problems in AI, engineering and the sciences are naturally...
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Learning, transferring, and recommending performance knowledge with Monte Carlo tree search and neural networks
Making changes to a program to optimize its performance is an unscalable...
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Combining Simulated Annealing and Monte Carlo Tree Search for Expression Simplification
In many applications of computer algebra large expressions must be simpl...
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Monte-Carlo Graph Search for AlphaZero
The AlphaZero algorithm has been successfully applied in a range of discrete domains, most notably board games. It utilizes a neural network, that learns a value and policy function to guide the exploration in a Monte-Carlo Tree Search. Although many search improvements have been proposed for Monte-Carlo Tree Search in the past, most of them refer to an older variant of the Upper Confidence bounds for Trees algorithm that does not use a policy for planning. We introduce a new, improved search algorithm for AlphaZero which generalizes the search tree to a directed acyclic graph. This enables information flow across different subtrees and greatly reduces memory consumption. Along with Monte-Carlo Graph Search, we propose a number of further extensions, such as the inclusion of Epsilon-greedy exploration, a revised terminal solver and the integration of domain knowledge as constraints. In our evaluations, we use the CrazyAra engine on chess and crazyhouse as examples to show that these changes bring significant improvements to AlphaZero.
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