Intermediate · Reinforcement Learning
Experience replay
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A technique in reinforcement learning that stores and samples past transitions to train agents.
Technical Definition
A technique in reinforcement learning that stores and samples past transitions to train agents.
How it works
Experience replay is a key technique used in Deep Q-Networks (DQN) to improve training stability in reinforcement learning. It involves storing past state transitions (experiences) in a buffer and then randomly sampling from this buffer to create training data, which helps to break correlations between consecutive samples.
Related Concepts
- Reinforcement Learning — A paradigm where an agent learns to make decisions by receiving rewards or penalties from its environment through trial and error.
- AI Agent — An AI system that autonomously plans, uses tools, and takes actions to accomplish goals through iterative reasoning.
- DQN — Deep Q-Network, a reinforcement learning algorithm combining deep learning with Q-learning for complex tasks.