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Reinforcement Learning

Reinforcement learning (RL) is a subfield of machine learning and artificial intelligence that focuses on how agents (e.g., robots, software agents, autonomous vehicles) can make sequences of decisions to maximize a cumulative reward in an environment. RL is inspired by behavioral psychology, where learning occurs through trial and error.

Key components of reinforcement learning include:

Agent: The entity or system that makes decisions and interacts with the environment.

Environment: The external system or surroundings in which the agent operates. It can be physical (e.g., a robot navigating a room) or virtual (e.g., a computer program playing a game).

State (s): A representation of the current situation or configuration of the environment that the agent perceives. It contains all relevant information needed to make decisions.

Action (a): The set of possible moves or decisions that the agent can take in a given state.

Policy (π): A strategy or set of rules that guides the agent's decision-making process. It defines how the agent selects actions based on states.

Reward (r): A numerical signal provided by the environment as feedback to the agent's actions. It indicates the immediate desirability or quality of the agent's current action in the current state.The primary goal of reinforcement learning is for the agent to learn an optimal policy, π*, that maximizes the expected cumulative reward over time. This is often referred to as the "reward signal" or "cumulative return." The agent explores different actions and learns from the consequences of those actions to improve its policy.

Reinforcement learning algorithms use various techniques to find the optimal policy, includin




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