Monto Carlo Tree Search in Real Time Strategy Games with Applications to Starcraft 2
Abstract: This thesis presents an architecture for an agent that can play the real-time strategy game Starcraft 2 (SC2) by applying Monte Carlo Tree Search (MCTS) together with genetic algorithms and machine learning methods. Together with the MCTS search, a light-weight and accurate combat simulator for SC2 as well as a build order optimizer are presented as independent modules. While MCTS has been well studied for turn-based games such as Go and Chess, its performance has so far been less explored in the context of real-time games. Using machine learning and planning methods in real-time strategy games without requiring long training times has proven to be a challenge. This thesis explores how a model based approach, based on the rules of the game, can be used to achieve a well performing agent.
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