  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.
