Reinforcement Learning

Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. The agent’s goal is to maximize cumulative rewards over time by learning optimal actions through trial and error. RL is particularly effective for sequential decision-making problems and is used in applications such as robotics, game playing, autonomous systems, and recommendation engines. Key components include the agent, environment, policy, reward signal and value function. Reinforcement learning enables adaptive behavior in complex, dynamic environments.