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An Analysis on the Effects of Evolving the Monte Carlo Tree Search Upper Confidence for Trees Selection Policy on Unimodal, Multimodal and Deceptive Landscapes
A selection policy that works particularly well in MCTS is the Upper Confidence Bounds for Trees, referred to as UCT.
Towards Understanding the Effects of Evolving the MCTS UCT Selection Policy
A particular selection policy that works particularly well, widely adopted in MCTS, is the Upper Confidence Bounds for Trees, referred to as UCT.
Evolving the MCTS Upper Confidence Bounds for Trees Using a Semantic-inspired Evolutionary Algorithm in the Game of Carcassonne
In this work, we use Evolutionary Algorithms (EAs) to evolve mathematical expressions with the goal to substitute the UCT formula and use the evolved expressions in MCTS.
Playing Carcassonne with Monte Carlo Tree Search
Monte Carlo Tree Search (MCTS) is a relatively new sampling method with multiple variants in the literature.
Statistical Tree-based Population Seeding for Rolling Horizon EAs in General Video Game Playing
Furthermore, we tackle the former limitation by employing a mechanism that allows us to seed part of the population using Monte Carlo Tree Search, a method that has dominated multiple General Video Game AI competitions.
