What does a DQN stand for in artificial intelligence?

A DQN stands for Deep Q-Network, which refers to a type of deep reinforcement learning algorithm. This approach combines deep learning and Q-learning to enable an agent to make decisions based on its observations of the environment.

In a Deep Q-Network, a deep neural network is used to approximate the Q-value function, which predicts the expected future rewards for state-action pairs. This allows the agent to learn optimal policies through experience, enabling it to make better decisions over time.

The term "Deep" indicates the use of neural networks with multiple layers to extract features from high-dimensional input spaces, while "Q-Network" signifies the association with Q-learning, a value-based reinforcement learning algorithm. The design of DQNs has been instrumental in achieving significant milestones in various complex tasks, such as playing video games and robotic control, demonstrating their effectiveness in solving real-world problems.