What is Reinforcement Learning?
TL;DR
An AI training method that learns optimal behavior through trial and error. Used in game AI and robotics control.
Reinforcement Learning: Definition & Explanation
Reinforcement Learning is a machine learning approach in which an agent (AI) learns a behavioral policy to maximize rewards through interaction with an environment. Rather than being given labeled training data as in supervised learning, the AI repeatedly tries different actions, receiving rewards for good outcomes and penalties for bad ones. It gained prominence through superhuman performance in AlphaGo (the Go-playing AI) and Atari games. In LLM training, reinforcement learning plays a crucial role through RLHF (Reinforcement Learning from Human Feedback), contributing significantly to the output quality of ChatGPT and Claude. Applications extend to robotics control, autonomous driving, recommendation systems, resource optimization, and other complex real-world decision-making problems.