Reinforcement Learning

Intermediate

A learning paradigm where an agent interacts with an environment and learns to choose actions to maximize cumulative reward.

Full Definition

A learning paradigm where an agent interacts with an environment and learns to choose actions to maximize cumulative reward.

Keywords

rewards policy environment

Domains

Reinforcement Learning

Related Terms

Machine Learning narrower than

A subfield of AI where models learn patterns from data to make predictions or decisions, improving with experience ra...

 Markov Decision Process prerequisite of

Formal framework for sequential decision-making under uncertainty.

 Policy used in

Strategy mapping states to actions.

 Value Function used in

Expected cumulative reward from a state or state-action pair.

 Agent related to

A system that perceives state, selects actions, and pursues goals—often combining LLM reasoning with tools and memory.

 System Prompt related to

A high-priority instruction layer setting overarching behavior constraints for a chat model.

 Guardrails related to

Rules and controls around generation (filters, validators, structured outputs) to reduce unsafe or invalid behavior.

 Model-Free RL related to

RL without explicit dynamics model.

 Model-Based RL related to

RL using learned or known environment models.

Concept Map

See how Reinforcement Learning connects to other concepts.

Open Knowledge Graph