Beginner · Reinforcement Learning
Episode
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A complete sequence of interactions between an agent and its environment in reinforcement learning, from start to finish.
Technical Definition
A complete sequence of interactions between an agent and its environment in reinforcement learning, from start to finish.
How it works
In reinforcement learning, an episode represents a single, complete run of an agent interacting with its environment until a terminal state is reached. For example, playing one full game of chess constitutes an episode. The agent learns from the sequence of states, actions, and rewards within each episode.
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.