In the field of artificial intelligence and machine learning, Deep Q Networks (DQN) are a class of deep reinforcement learning algorithms. DQN is a neural network-based method for handling discrete action spaces and high-dimensional state spaces in reinforcement learning problems. DQN blends deep neural networks with Q-learning, a conventional reinforcement learning method, to produce state-of-the-art outcomes in a variety of fields, such as robotics and video games.
That's accurate. One of the main goals of training Deep Q Networks (DQN) and other Q-learning based reinforcement learning algorithms is to minimize a loss function that measures the variation between the target Q-values (the target Q-value estimate) and the estimated Q-values (the current Q-value estimate). A key idea in reinforcement learning, the temporal difference (TD) error, is frequently the foundation of this loss function.
The difference between the target Q-value and the estimated Q-value (Q(s, a)) for a state-action pair (s, a) is known as the TD error. The Bellman equation, which expresses the expected cumulative reward when taking action "a" in state "s" and subsequently adhering to the best course of action, is commonly used to determine the target Q-value.
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