I am curious about how we can address the exploding gradient problem in deep reinforcement learning (DRL), particularly within Deep Q-Network (DQN) algorithms. Could you explain the exploding gradient problem in the context of DRL? Is it possible for DQN algorithms to face the same challenges with exploding gradients that are observed in deep learning?

In deep learning, the exploding gradient problem arises when gradients become excessively large during backpropagation, causing drastic updates to the neural network weights and leading to unstable training. This issue often occurs in very deep networks, where the repeated multiplication of gradients causes their exponential growth.

Does this problem also occur in DRL, where deep neural networks are used for approximating value functions or policies? Specifically, do DQN algorithms, which rely on deep neural networks to estimate action values, encounter the exploding gradient problem, especially when handling high-dimensional state and action spaces or when the learning rate is not adequately controlled?