Improving Monte Carlo Tree Search Policies in StarCraft via Probabilistic Models Learned from Replay Data

Authors

  • Alberto Uriarte Drexel University
  • Santiago Ontañón Drexel University

DOI:

https://doi.org/10.1609/aiide.v12i1.12852

Keywords:

RTS, real-time strategy games, game-tree search, StarCraft, bot, AI, BWAPI

Abstract

Applying game-tree search techniques to RTS games poses a significant challenge, given the large branching factors involved. This paper studies an approach to incorporate knowledge learned offline from game replays to guide the search process. Specifically, we propose to learn Naive Bayesian models predicting the probability of action execution in different game states, and use them to inform the search process of Monte Carlo Tree Search. We evaluate the effect of incorporating these models into several Multiarmed Bandit policies for MCTS in the context of StarCraft, showing a significant improvement in gameplay performance.

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Published

2021-06-25

How to Cite

Uriarte, A., & Ontañón, S. (2021). Improving Monte Carlo Tree Search Policies in StarCraft via Probabilistic Models Learned from Replay Data. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 12(1), 100-106. https://doi.org/10.1609/aiide.v12i1.12852