Prioritized Experience Replay (PER) is a technique used in reinforcement learning to improve the efficiency of training by focusing on more important experiences. In traditional experience replay methods, an agent learns from experiences sampled randomly from a replay buffer. However, PER enhances this process by assigning a priority to each experience based on its significance for learning. This means that experiences that are more informative or surprising have a higher chance of being replayed during training, leading to better performance and faster convergence of the agent.
In practice, PER uses a priority sampling method where experiences in the replay buffer are given a score based on their Temporal-Difference (TD) error. The TD error represents how far off a predicted action's value was from the actual outcome. Experiences with a higher TD error indicate that the agent struggled with that particular observation, making those experiences more crucial for learning. By sampling experiences with higher priorities more frequently, the model can quickly learn from its mistakes and improve its decision-making abilities. A common implementation is to use a proportional method where the probability of sampling an experience is proportional to its priority.
Implementing PER requires modifying the experience replay mechanism used in conventional methods. It typically involves maintaining a priority queue and an additional structure to normalize priorities, ensuring a balanced sampling approach that avoids bias towards high-reward experiences. Popular libraries like TensorFlow and PyTorch have implementations that make it easier for developers to integrate PER into their workflows. By using this technique, developers can enhance the learning efficiency of their agents and improve overall performance on complex tasks, making it a valuable addition to many reinforcement learning systems.