Beginner · Reinforcement Learning
Error-driven learning
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A machine learning approach where an agent learns by minimizing errors caused by its actions in an environment.
Technical Definition
A machine learning approach where an agent learns by minimizing errors caused by its actions in an environment.
How it works
Error-driven learning is a fundamental concept, particularly in reinforcement learning. It describes a process where an agent adjusts its behavior based on the difference between its expected outcome and the actual outcome of its actions. The goal is to reduce these errors over time to improve performance.
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.
- Machine Learning — A field of AI where systems learn patterns from data instead of following hard-coded rules.
- Feedback loop — A feedback loop occurs when a model's output influences its future input or training data, potentially leading to system drift or reinforcement of biases.