Playing Fight the Landlord with Tree Search and Hidden Information Evaluation
Zeya He, Xiaochuan Zhang, Xin Wang, Liu Liu, Xinlai Xing · 2022
Monte Carlo Tree Search (MCTS) method is used in many games, such as Chess, Go, etc., but MCTS cannot be directly applied to Fight the Landlord games. Accurately evaluate actions in any game state is one of the keys to master Fight the Landlord. In this paper, we investigate a method that combines neural networks and MCTS to make agents play Fight the Landlord smarter. And we used supervised learning, reinforcement learning with self-play methods to train agents. Our experiments show that the method proposed in this paper is effective, and we compared it with some open-source Fight the Landlord AI programs, the method proposed in this paper is still powerful.