Reinforcement Learning
Applications of Reinforcement Learning Reinforcement Learning (RL) is a subfield of machine learning in which an intelligent agent learns optimal behavior by interacting with an environment and receiving feedback in the form of rewards or penalties. Unlike supervised learning, RL does not rely on labeled datasets; instead, it focuses on sequential decision-making and long-term reward maximization. Due to these characteristics, RL is widely applied in domains requiring adaptive, autonomous, and optimal control. 1. Robotics and Autonomous Systems Reinforcement Learning enables robots to learn complex motor skills and decision-making policies through trial and error. Key Applications: Robot navigation and obstacle avoidance Robotic arm manipulation and grasping Autonomous drones and warehouse robots Human–robot interaction Example: Robots learning to walk, climb stairs, or pick and place objects without explicit programming. 2. Game Playing and Simulation RL has achieved remarkable...
