Hexagon Puzzle Game AI: Design & Neural Network Input

One way to input the game state to the neural network is to represent the current board as a matrix or tensor, where each element represents a hexagon piece and its type. The matrix/tensor can be resized as the board expands.

For example, if the initial board has only one hexagon piece, the matrix/tensor would be a 1x1 matrix/tensor. As more pieces are added, the matrix/tensor would expand accordingly.

In addition to the current board state, the neural network can also take in the remaining pieces, the number of completed quests, and the current score as inputs.

The output of the neural network can be the next hexagon piece to be placed on the board. The network can be trained using reinforcement learning, where the reward is based on the score obtained from the current board state.