Deep reinforcement learning

Deep reinforcement learning (DRL) is a subfield of machine learning that combines principles of reinforcement learning (RL) and deep learning. It involves training agents to make decisions by interacting with an environment to maximize cumulative rewards, while using deep neural networks to represent policies, value functions, or environment models. This integration enables DRL systems to process high-dimensional inputs, such as images or continuous control signals, making the approach effective for solving complex tasks. Since the introduction of the deep Q-network (DQN) in 2015, DRL has achieved significant successes across domains including games, robotics, and autonomous systems, and is increasingly applied in areas such as healthcare, finance, and autonomous vehicles.