Position-based reinforcement learning biased MCTS for General Video Game Playing
Chun Yin Chu, Suguru Ito, Tomohiro Harada, Ruck Thawonmas · 2016
This paper proposes an application of reinforcement learning and position-based features in rollout bias training of Monte-Carlo Tree Search (MCTS) for General Video Game Playing (GVGP). As an improvement on Knowledge-based Fast-Evo MCTS proposed by Perez et al., the proposed method is designated for both the GVG-AI Competition and improvement of the learning mechanism of the original method. The performance of the proposed method is evaluated empirically, using all games from six training sets available in the GVG-AI Framework, and the proposed method achieves better scores than five other existing MCTS-based methods overall.