Deep Q Network
Deep Q Network (DQN) is a reinforcement learning algorithm that combines Q-learning, a value-based method for learning optimal policies, with deep neural networks to handle high-dimensional state spaces, such as images or complex sensor data. It uses a neural network as a function approximator to estimate Q-values, which represent the expected future rewards for taking actions in given states, enabling agents to learn directly from raw sensory inputs without manual feature engineering. DQN introduced key innovations like experience replay and target networks to stabilize training and improve convergence in deep reinforcement learning tasks.
Developers should learn DQN when building AI agents for environments with large or continuous state spaces, such as video games, robotics, or autonomous systems, where traditional tabular Q-learning is infeasible. It is particularly useful for applications requiring agents to learn from pixel-based inputs or complex sensor data, as demonstrated in benchmarks like Atari games, making it a foundational technique for deep reinforcement learning research and practical implementations. DQN serves as a stepping stone to more advanced algorithms and is essential for understanding how to scale reinforcement learning to real-world problems.