Dissertation
Integrating domain knowledge into Monte Carlo Tree Search for real-time strategy games
Doctor of Philosophy (Ph.D.), Drexel University
Jun 2022
DOI:
https://doi.org/10.17918/00001114
Abstract
Tree search algorithms are widely applied methods to model and solve sequential decision problems. In particular, the family of sampling-based tree search algorithms called Monte Carlo Tree Search (MCTS) has had great success in problems with large branching factors. However, Real-Time Strategy (RTS) games offer a challenging testbed for tree search algorithms due to their large combinatorial action spaces, partial observability, simultaneous moves, and other factors, making them beyond the grasp of even current MCTS algorithms. This thesis makes contributions towards scaling MCTS algorithms to become more effective and efficient in the domain of RTS games. Specifically, this thesis contributes on the following problems. Firstly, we explore the problem of the integration of MCTS and domain knowledge, in the form of unit-action probability distributions, state evaluation functions, and scripted bots. Secondly, we investigate the optimization of gameplay/rollout policies for MCTS. Third, we study methods for self-learning in MCTS, where tree and/or rollout policies are bootstrapped directly from MCTS behavior iteratively.
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Details
- Title
- Integrating domain knowledge into Monte Carlo Tree Search for real-time strategy games
- Creators
- Zuozhi Yang
- Contributors
- Santiago Ontañón (Advisor)
- Awarding Institution
- Drexel University
- Degree Awarded
- Doctor of Philosophy (Ph.D.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- viii, 106 pages
- Resource Type
- Dissertation
- Language
- English
- Academic Unit
- Computer Science (Computing) (2013-2026); College of Computing and Informatics (2013-2026); Drexel University
- Other Identifier
- 991018527910304721