3. Determining the Right Number of Hidden Layers

Deciding on the number of hidden layers in a neural network, such as the one used in Deep Q-Learning for the Snake game, involves a balance between underfitting and overfitting, as well as computational considerations. Here are some guidelines to help you decide on the number of hidden layers:

1. Problem Complexity

Simple Problems: For simple problems with low-dimensional input data and straightforward relationships, a neural network with 1 or 2 hidden layers may be sufficient.
Complex Problems: More hidden layers may be needed to capture the underlying structure for more complex problems with high-dimensional input data and intricate patterns.

2. Empirical Testing

Experimentation: Start with a small number of hidden layers (1 or 2) and gradually increase the number, monitoring performance on a validation set.
Hyperparameter Tuning: Use hyperparameter tuning techniques such as grid or random search to find the optimal number of hidden layers and neurons per layer.

3. Avoiding Overfitting

Regularization: More hidden layers can lead to overfitting, especially with limited training data. Techniques like dropout, L2 regularization, and early stopping can help mitigate overfitting.
Cross-Validation: Use cross-validation to assess the model’s performance and generalization capability.

4. Computational Resources

Training Time: More hidden layers increase the parameters, leading to longer training times. Ensure you have sufficient computational resources to handle the increased complexity.
Inference Speed: If the model is to be deployed in a real-time setting, consider the inference speed and latency.

5. Domain Knowledge

Insights from Literature: Review similar problems in the literature to get an idea of what architectures have been successful.
Prior Experience: Use knowledge from previous projects or related domains to inform your decision.