Reinforcement Learning

Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to make optimal decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. Unlike supervised learning, which learns from labeled examples, reinforcement learning discovers effective strategies through trial and error, gradually improving its performance based on the consequences of its actions. The agent seeks to maximize cumulative rewards over time by learning which actions lead to positive outcomes in different situations. RL has achieved remarkable successes in complex domains including game playing (AlphaGo, chess), robotics, autonomous vehicles, and resource optimization. Key concepts include the exploration-exploitation tradeoff, where the agent must balance trying new actions versus exploiting known good strategies. Modern RL often uses deep neural networks (deep reinforcement learning) to handle complex state spaces and has been instrumental in training AI systems that can adapt and improve their performance in dynamic environments.