Reinforcement learning trains agents to make optimal decisions through trial and error interactions with environments. Agents receive rewards or penalties based on their actions, gradually learning policies that maximize cumulative rewards. Q-learning and policy gradient methods are fundamental approaches. Applications include game playing (AlphaGo), robotics control, autonomous driving, recommendation systems, and financial trading algorithms. The exploration-exploitation trade-off remains a central challenge.