RL

Reinforcement learning is a type of machine learning where an algorithm learns to make sequential decisions by interacting with its environment and receiving feedback in the form of rewards or penalties.

Unlike supervised or unsupervised learning, reinforcement learning focuses on learning optimal actions through trial and error in order to maximize long-term cumulative rewards.

A reinforcement learning system typically consists of an agent, an environment, a policy, a reward function, and optionally a value function and model.

An agent is the entity that learns and interacts with the environment. It takes actions based on its policy and receives feedback from the environment in the form of rewards or penalties.

An environment represents the external system with which the agent interacts. It provides feedback to the agent based on its actions and can be any scenario or domain like games, robotics, or simulations.

A policy is a strategy that the agent uses to determine its actions based on its current state. It maps states to actions and guides the agent in making decisions.

A reward function defines the goal or objective of a reinforcement learning task. It assigns scalar values as rewards or penalties to the agent based on its actions and state transitions.

A value function estimates the expected long-term rewards for an agent in a given state or state-action pair. It helps the agent make decisions by providing insights into the potential outcomes of different actions.

A model in reinforcement learning represents the agent's understanding or approximation of the environment's dynamics. It can be used for planning, predicting outcomes, or generating synthetic experience.

Popular reinforcement learning algorithms include Q-learning, SARSA, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Trust Region Policy Optimization (TRPO), among others.