Experience replay is an important technique used in Deep Q-Networks (DQN) to improve the efficiency and stability of the learning process. The basic idea behind experience replay is to store a collection of past experiences (or transitions) observed by the agent in a finite-sized replay buffer and randomly sample a subset of these experiences to train the model instead of learning from the data as it comes.
This approach allows the DQN model to improve its learning by breaking the correlation between consecutive experiences, which in turn, reduces the volatility of training and enables the agent to learn from a wider range of different scenarios. It can also help prevent the model from overfitting to specific experiences and allows the agent to learn from rare events, that it otherwise may not encounter frequently enough in the real world through random sampling.
The replay buffer typically stores tuples of experiences (S,A,R,S’) observed by the agent, where S is the observation, A is the action taken, R is the reward received, and S’ is the next observation after taking the action. During each training iteration, a batch of experiences are randomly sampled from the replay buffer, and these transitions are then used to update the DQN model. By constantly updating the model based on random experiences, the DQN can learn from a more diverse set of scenarios and improve its overall performance.
For example, suppose we have an agent that is learning to navigate an environment in a game. During its exploration, the agent collects a set of experiences that are stored in the replay buffer. When the agent is ready to train the DQN model, it randomly samples a batch of experiences from the replay buffer and uses them to update the model. The agent might sample an experience where it successfully navigated to the goal, and another experience where it got stuck in a corner. The agent then uses the combination of these experiences to update its policy, ensuring that it learns from different scenarios and is better prepared to handle unseen situations in the future.
Overall, experience replay is a vital component of the DQN algorithm, helping the agent learn from a diverse set of past experiences while breaking the correlation between consecutive transitions. By training the model on randomly sampled experiences, the agent can improve its performance and training stability.