Explain the concept of 'experience replay' and its purpose in Deep Q-Networks (DQNs).

Experience replay is a technique used in Deep Q-Networks (DQNs) to improve the stability and efficiency of the learning process. In standard Q-learning, the agent learns directly from its experiences as they occur, updating the Q-values after each step. However, this can lead to unstable learning because consecutive experiences are often highly correlated, and small changes in the Q-values can have a significant impact on the policy. Experience replay addresses this issue by storing the agent's experiences in a replay buffer, which is a memory bank of past transitions. Each transition consists of the state, action, reward, and next state (s, a, r, s'). During training, instead of learning from the most recent experience, the DQN samples a random batch of transitions from the replay buffer and uses these samples to update the Q-values. This has several benefits. First, it breaks the correlation between consecutive experiences, reducing the variance in the updates and stabilizing learning. Second, it allows the agent to learn from past experiences multiple times, improving the sample efficiency of the learning process. Third, it smooths the training distribution over the agent's experiences, avoiding situations where the agent gets stuck in a local optimum due to a biased sequence of experiences. For example, if a DQN is learning to play a video game, the replay buffer would store the agent's moves, the resulting rewards, and the changes in the game state. During training, the DQN would randomly sample batches of these experiences from the buffer to update its Q-values, effectively replaying past scenarios to learn from them more effectively. Therefore, experience replay is a crucial component of DQNs, enabling them to learn stable and effective policies in complex environments.