Optimizing Hexagon Puzzle Game AI with Machine Learning (MCTS)

One way to incorporate machine learning to improve the result is to use a neural network to evaluate the state of the game. You can train the neural network with a dataset of game states and their corresponding values. The neural network can then be used to estimate the value of a given game state, which can be used as a heuristic to guide the Monte Carlo tree search. This can help to reduce the search space and improve the efficiency of the search, leading to better results. Another approach is to use reinforcement learning to train an agent to play the game. The agent can then be used to guide the search and suggest moves to the player. This can also help to improve the efficiency of the search and lead to better results.