Reinforcement Learning Vocabulary

Vocabulary terms related to reinforcement learning concepts and techniques.

Reinforcement Learning

A category of machine learning where an agent learns to make decisions by receiving rewards from the environment based on its actions.

Supervised Learning

A type of machine learning where the model is trained on labeled data, learning input-output mappings.

Unsupervised Learning

A type of machine learning where the model learns patterns from data without labeled responses.

Agent

An entity that interacts with an environment in reinforcement learning to achieve a goal.

Rewards

Feedback received by the agent in reinforcement learning, which can be positive or negative, guiding its learning.

Markov Decision Process (MDP)

A mathematical framework used in reinforcement learning to describe a decision-making environment where outcomes are partly random and partly under the control of a decision maker.

Action-Utility Learning

A model-free approach in reinforcement learning where the agent learns the value of actions in achieving rewards.

Policy Search

A method in reinforcement learning where the agent learns a mapping from states to actions to maximize cumulative rewards.

Temporal-Difference Learning

A reinforcement learning method that combines ideas from Monte Carlo methods and dynamic programming, learning how to predict future rewards based on current estimates.

Q-Learning

A popular model-free reinforcement learning algorithm that learns the value of action in particular states to inform future actions.

Model-Free Learning

Learning approaches in reinforcement learning where the agent learns to act directly without building a model of the environment.

Model-Based Learning

Learning approaches in reinforcement learning where the agent learns a model of the environment to inform its actions.

Exploration

The process by which an agent tries new actions to discover their potential rewards, balancing the trade-off between exploration and exploitation.

Greedy Agent

An agent that always chooses the action that currently appears to be the best based on its learned values.

Generalization

The ability of a reinforcement learning agent to apply learned knowledge from known states to new, unseen states.

Feature-Based Evaluation

A method of approximating utilities or Q-values using a set of features that describe states or actions.

Deep Reinforcement Learning

A combination of reinforcement learning with deep learning, employing neural networks to learn action-value functions or policies.