Deep Reinforcement Learning

Deep Reinforcement Learning combines neural networks (deep learning) with reinforcement learning principles. An agent learns to perform tasks by interacting with an environment, making decisions, and receiving feedback in the form of rewards or penalties. Over time, it recognizes patterns and strategies to maximize rewards. The neural network helps the agent process complex data and make informed decisions, enabling the system to handle tasks like game playing, robotics, and autonomous vehicles efficiently. It’s like teaching a computer to learn from experience, improving its actions without explicit programming for every scenario.