Beginner · Reinforcement Learning
Action
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. The mechanism by which an agent in reinforcement learning changes the state of its environment.
Technical Definition
The mechanism by which an agent in reinforcement learning changes the state of its environment.
How it works
In reinforcement learning, an action is a move or decision made by an agent that influences the state of its environment. The agent selects actions based on its current policy, aiming to maximize its cumulative reward over time. The set of all possible actions an agent can take is defined by its action space.
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.
- Policy — In reinforcement learning, the strategy an agent follows to choose actions given a state.