Deep Q-Networks

Deep Q-Networks (DQN) are a type of deep reinforcement learning algorithm that combines Q-learning with deep neural networks. They are designed to learn optimal action-selection policies in environments where the state and action spaces are large and complex. DQNs utilize experience replay and target networks to stabilize training and improve learning efficiency. This approach has been widely adopted in various applications such as game playing, robotics, and autonomous systems, showcasing the ability to learn from high-dimensional sensory inputs. DQNs have achieved remarkable success in environments like Atari games, where they can outperform human players by learning directly from raw pixel data.