Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is a subset of machine learning that combines reinforcement learning principles with deep learning techniques. In DRL, agents learn to make decisions by interacting with their environment, receiving feedback in the form of rewards or penalties. This approach enables the agent to discover optimal strategies for achieving specific goals through trial and error. Common characteristics of DRL include the use of neural networks to approximate value functions and policies, as well as the ability to handle high-dimensional state spaces. DRL is widely used in applications such as robotics, gaming, and autonomous systems, where complex decision-making is required.