Deep Reinforcement Learning (DRL)

In DRL, an AI agent observes its environment, takes actions, and receives rewards or penalties based on the results. Deep neural networks help the agent process complex inputs and learn which actions are most likely to maximize its long-term reward. Through repeated interactions, the agent gradually develops strategies for completing tasks without requiring explicit instructions for every situation.

DRL enables AI systems to learn complex decision-making strategies through experience. It is particularly valuable for problems involving large state spaces, sequential decisions, and changing environments where manually programming every possible action would be impractical.