Reinforcement Learning Agent Playing Go-Moku with Neural Network

This is used to generate imitation learning data and serve as an opponent for reinforcement learning.- Design a DQN agent that clones the behavior of the rule-based player using game data, then plays against the rule-based agent via reinforcement learning.

The agent receives rewards for winning, with extra rewards given for generating live three, live four, and double live three patterns. These experiences are stored in a replay buffer and continuously replayed during training.- Save the trained DQN model to disk and employ Monte Carlo tree search combined with prior knowledge and evaluation networks to look ahead 5–7 moves, significantly improving move quality.- Develop a FastAPI web-based gaming service supporting human vs. AI matches, allowing users to switch between different AI types and adjust search depth.