Experience Replay

Gold definitionUpdated Apr 2, 2026

Experience Replay (ER) is a fundamental mechanism in machine learning, primarily employed in reinforcement learning (RL) and continual learning (CL) to stabilize training and prevent catastrophic forgetting. It works by storing a collection of past 'experiences' (e.g., state-action-reward-next_state tuples in RL, or input-output pairs in CL) in a memory buffer. During training, instead of only using the most recent data, the model samples mini-batches from this buffer, effectively re-exposing it to previously encountered information. This repeated rehearsal helps to decorrelate training samples, smooth out learning updates, and reinforce learned knowledge, thereby balancing the model's stability (retaining old knowledge) and plasticity (learning new tasks). ER is widely used in deep reinforcement learning algorithms like DQN and in various continual learning strategies for large language models and other neural networks, addressing the challenge of learning sequentially without forgetting prior tasks.

Core Mechanism of Experience Replay

Memory Buffer: ER maintains a memory buffer (or replay buffer) that stores a fixed number of past experiences. These experiences can be state transitions in reinforcement learning or input-output pairs in continual learning scenarios.
Sampling Strategy: During training, instead of processing data sequentially, mini-batches are randomly sampled from the experience replay buffer. This random sampling decorrelates successive updates, which is crucial for stability in off-policy learning algorithms.
Catastrophic Forgetting Mitigation: By re-exposing the model to old data, ER acts as a standard countermeasure against catastrophic forgetting, a critical challenge in continual learning where models rapidly lose performance on previous tasks when learning new ones.

Dichotomy and Challenges of Experience Replay

At a glance

Executive summary

Experience Replay is a technique that helps AI models remember past lessons by storing and re-using old data during training. This prevents the model from forgetting what it learned previously when it's taught new things, though it can sometimes struggle with complex, structured tasks.

TL;DR

Experience Replay helps AI models remember old information by replaying past experiences, preventing them from forgetting when learning new tasks.

Key points

Stores past experiences in a memory buffer and samples them for repeated training.
Solves catastrophic forgetting in continual learning and stabilizes training in reinforcement learning.
Used extensively in deep reinforcement learning (e.g., DQN) and continual learning for LLMs.
Unlike regularization methods that constrain weight updates, ER directly re-exposes the model to past data.
Research trend focuses on adaptive sampling strategies and addressing its limitations in structured domains.

Use cases

Training autonomous agents in complex environments (e.g., Atari games, robotics) to learn from past interactions.
Enabling Large Language Models to continually learn new facts or skills without forgetting previous knowledge.
Developing personalized recommendation systems that adapt to new user preferences while retaining knowledge of past tastes.
Training models for sequential decision-making in financial trading or resource management.
Robotics learning from demonstration, where observed actions are stored and replayed to improve policy.

Also known as

ER, Replay Buffer, Prioritized Experience Replay (PER), Hindsight Experience Replay (HER), Reservoir Replay