In Reinforcement Learning (RL), the architecture of the neural network (also known as the function approximator) can significantly impact the agent's ability to learn an optimal policy. Here are a few ways the size and architecture of the neural network can affect the learning process:

Impact of Neural Network Size on RL:

1. Capacity: A larger network typically has a higher capacity, which means it can potentially learn a more complex policy. However, if the network is too large, it may overfit the training data and perform poorly on unseen states.

2. Training Time: Larger networks require more training and computational resources.

3. Stability: The architecture can impact the stability of the training process. Deep networks are often harder to train due to issues like vanishing or exploding gradients, although techniques like batch normalization and careful initialization can help.

4. Generalization: The size and structure of the network influence how well the agent can generalize its policy to new states. A smaller network may generalize better but might not be able to capture the complexity of the optimal policy.

5. Sample Efficiency: A more complex model might require more samples to achieve a good approximation, which could be expensive regarding computational time or real-world interactions.

Deep Recurrent Q-Networks:

In the case of Deep Recurrent Q-Networks (DRQN), which extend DQNs using recurrent layers like LSTM or GRU, the recurrent layer's size can also impact the agent's ability to consider temporal dependencies in partially observable environments.

Recommended Papers:

While papers provide valuable insights, the best architecture for your problem may require empirical testing. You may need to experiment with various architectures and hyperparameters to find the one that works best for your problem.