Beginner · Reinforcement Learning
Environment
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. The external world or system with which an agent in reinforcement learning interacts and receives feedback.
Technical Definition
The external world or system with which an agent in reinforcement learning interacts and receives feedback.
How it works
In reinforcement learning, the environment is the setting where an agent operates and learns. The agent observes the state of the environment, takes actions, and in return, receives feedback in the form of rewards or penalties and transitions to a new state. The environment defines the rules and dynamics of the learning problem.
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.