Reinforcement learning is a training approach where an AI system learns by taking actions in an environment and receiving feedback — rewards for good outcomes, penalties for bad ones. Over many iterations, the system learns which actions tend to produce better results.
It is used to train agents that need to improve over time in dynamic or goal-driven environments where the correct behavior cannot be fully specified in advance. Game-playing AI and robotics control are classic examples; in enterprise settings, it is used for recommendation systems, scheduling optimization, and training agents for complex decision tasks.
