Designing a neural network for a Snake game AI

Other Questions AI Agent
1

Hey everyone! I’m trying to create an AI that can play Snake, and I need some advice on how to design the neural network. Currently, my setup includes a 3-layer, 10x10 grid as the input (one layer for the snake’s body, one for the apple, and one for the snake’s head), and the output is a vector with 4 values corresponding to the possible moves: up, down, left, and right.

I’m marking the grid using 0s and 1s. Is this a good method to capture the game state? I’d love suggestions on how to refine my approach or pick a better network structure.

# New sample input structure
game_input = [
    [[0, 1, 0, 0, ...], [1, 0, 1, 0, ...], ...],  # Body grid
    [[0, 0, 1, 0, ...], [0, 1, 0, 0, ...], ...],  # Apple grid
    [[1, 0, 0, 1, ...], [0, 0, 1, 0, ...], ...]   # Head grid
]

# New sample output vector
move_output = [0, 0, 1, 0]  # Example: move left

Thanks for any insights!

2

As someone who’s dabbled in game AI, I can share a few thoughts on your Snake AI approach. Your input representation is solid, but you might want to consider simplifying it. Instead of three separate grids, you could use a single 10x10 grid with different values for the snake body, head, and apple. This could make your input more compact without losing important information.

For the network structure, I’ve found that adding a few convolutional layers can really help with grid-based games like Snake. They’re great at picking up on spatial patterns, and incorporating dropout layers can help prevent overfitting. Also, sometimes including a few pre-processed features such as the distance to the apple or potential danger zones can boost performance. Experiment with different architectures, and best of luck with your project!

3

hey, u might try a combined grid to simplify input and give network memory with LSTM layers for past context.

sure could reduce complexity and improve decisions. adding a bit of randomness in training may help avoid stagnation. good luck!

4

Your approach to representing the game state is on the right track, but there might be room for optimization. Instead of using three separate 10x10 grids, you could potentially consolidate this into a single 10x10 grid with different values representing the snake’s body, head, and apple. This could reduce the input complexity without losing essential information.

For the network structure, consider using convolutional layers. These are particularly effective for processing grid-based inputs and could help your model learn spatial relationships more efficiently. You might also want to experiment with adding a few fully connected layers after the convolutional layers to process the extracted features.

Regarding the output, your current setup is logical. However, you might want to consider using a softmax activation for the output layer to get probabilities for each move. This could provide more nuanced decision-making capabilities for your AI.

Lastly, don’t forget to incorporate some form of reward system in your training process. This could significantly improve the AI’s performance over time.