Reinforcement Learning

Reinforcement Learning (RL) is a type of Machine Learning where an agent learns to make decisions by interacting with an environment. The agent receives rewards for good actions and penalties for bad ones, gradually learning optimal strategies.

Core Concepts

Agent: The learner or decision-maker.
Environment: The world the agent interacts with.
Actions: Choices the agent can make.
Rewards: Feedback signals that guide learning.
Policy: The strategy used by the agent to choose actions.
Value Function: Predicts expected future rewards for states or actions.

Applications of Reinforcement Learning

Game AI: training agents to play chess, Go, or video games at superhuman levels.
Robotics: teaching robots to navigate, grasp objects, or perform complex tasks.
Autonomous Vehicles: optimizing driving policies and traffic management.
Finance: dynamic trading strategies and portfolio optimization.
Healthcare: treatment planning and adaptive interventions.