Reinforcement Learning (RL)

These terms collectively describe the key components and concepts of reinforcement learning, illustrating how agents learn to make decisions through trial and error, guided by the feedback received from their interactions with the environment.

Agent - In the context of reinforcement learning, an agent is an entity that makes decisions by interacting with an environment to achieve certain goals or maximize a reward signal.

Exploitation - This concept involves an AI system maximizing its performance based on known information, essential for optimizing actions in environments with defined rules and rewards.

Exploration - This process is critical for an AI system to discover new strategies and information, necessary for adapting to new or dynamic environments.

Markov Decision Process (MDP) - A mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision-maker, fundamental in formalizing reinforcement learning problems.

Policy or Q-function - Represents the strategy that the agent employs to determine the next action based on the current state, with Q-function specifically referring to the action-value function that estimates the value of taking an action in a given state.

Reinforcement Learning (RL) - A type of machine learning where an agent learns to make decisions by performing actions and receiving feedback in the form of rewards or penalties, focusing on learning optimal policies for decision-making.

Reward Signal - The feedback that an agent receives from the environment to evaluate the actions it has taken, guiding the learning process by indicating the desirability of an outcome.

Value Function - A function that estimates the expected return (cumulative discounted reward) of being in a state, under a particular policy, guiding the agent's decision-making process in reinforcement learning.