Reinforcement Learning Agents

Reinforcement learning is a machine learning paradigm where agents learn to take actions in an environment to maximize cumulative rewards. The agent doesn't receive labeled training data; instead, it interacts with an environment, receives reward signals, and adjusts its policy (decision-making strategy) to improve over time. Classic RL applications: game-playing (AlphaGo, Atari), robotics (motion control), optimization (resource allocation), and autonomous systems.