How do I input the game state to the neural network as the board keep increasing after every step

There are different ways to input the game state to a neural network when the board keeps increasing after every step. Here are some possible approaches:

1. Fixed-size input: You can define a fixed-size input for the neural network that can handle the largest board size expected in the game. For example, if the board size can be up to 10x10, you can define an input of size 10x10x3 (assuming a 3-channel input for RGB colors or player tokens). Then, you can pad the smaller boards with zeros or a neutral color to match the fixed-size input. This approach is simple but may waste some input space and computational resources.

2. Dynamic input: You can define a dynamic input for the neural network that adapts to the current board size. For example, you can pass the board size as an additional input to the neural network and use it to adjust the input size accordingly. Then, you can crop or resize the board image to fit the input size. This approach is more flexible but may require more preprocessing and input handling.

3. Recursive input: You can define a recursive input for the neural network that processes the board state recursively as it grows. For example, you can use a convolutional neural network (CNN) that processes a smaller board size and applies it recursively to larger board sizes. Then, you can concatenate the feature maps from each level of recursion to form a final representation of the board state. This approach is more complex but may capture the hierarchical structure of the game state more effectively.

Ultimately, the choice of input representation depends on the specific game mechanics, available data, and computational resources. It may also require experimentation and tuning to find the most effective approach