Exploring Adaptive MCTS with TD Learning in miniXCOM
Date
2022-10-24
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Abstract
In recent years, Monte Carlo tree search (MCTS) has achieved widespread adoption within the game community. Its use in conjunction with deep reinforcement learning has produced success stories in many applications. While these approaches have been implemented in various games, from simple board games to more complicated video games such as StarCraft, the use of deep neural networks requires a substantial training period. In this work, we explore on-line adaptivity in MCTS without requiring pre-training. We present MCTS-TD, an adaptive MCTS algorithm improved with temporal difference learning. We demonstrate our new approach on the game miniXCOM, a simplified version of XCOM, a popular commercial franchise consisting of several turn-based tactical games, and show how adaptivity in MCTS-TD allows for improved performances against opponents.
Description
Proceedings of the Ninth AIIDE Workshop on Experimental Artificial Intelligence in Game (EXAG 2022)
Keywords
Artificial Intelligence, Monte Carlo Tree Search, Reinforcement Learning
Citation
Saadat, K., and Zhao, R. (2022, October 24-25). Exploring Adaptive MCTS with TD Learning in miniXCOM [Paper presentation]. AIIDE Workshop on Experimental AI in Games (EXAG) 2022, Pomona, CA, United States.