What is Experience Replay?
Experience Replay is a crucial technique used in Reinforcement Learning (RL) to enhance the training of agents. In traditional RL, agents learn from their experiences sequentially, which can lead to inefficient learning and poor convergence. Experience Replay addresses these issues by storing past experiences in a memory buffer and allowing the agent to learn from a randomized sample of these experiences during training.
The core idea is to break the temporal correlations encountered in sequential learning. By re-sampling experiences, the agent can learn more effectively from a diverse set of past interactions, which aids in stabilizing and accelerating the learning process. This technique is particularly beneficial in environments where the state space is large, and sample efficiency is critical.
Typically, an agent will gather experiences in the form of tuples, which include the current state, action taken, reward received, and the next state. These tuples are then stored in a replay buffer. During training, random samples from this buffer are used to update the agent's policy, ensuring that the learning algorithm has access to a wide variety of experiences.
Overall, Experience Replay is an essential strategy that improves the performance of reinforcement learning algorithms, leading to more effective decision-making in complex environments.