Reinforcement Learning

An ML paradigm where an agent learns optimal behavior through trial-and-error interactions with an environment. By performing actions and receiving feedback in the form of rewards or penalties, the agent identifies strategies that maximize cumulative rewards over time. This learning process enables the agent to make informed decisions and adapt its actions to achieve specific goals. Reinforcement learning is pivotal in areas requiring decision-making under uncertainty, such as robotics, game playing, and autonomous vehicles, empowering agents to autonomously improve their performance based on direct experiences.