In deep reinforcement learning, what aspect does the DQN improve upon compared to traditional methods?

Deep Q-Networks (DQN) improve upon traditional reinforcement learning methods primarily by utilizing neural networks to approximate the Q-values, which indeed eliminates the need for a Q-table. In traditional Q-learning, the agent maintains a Q-table that explicitly maps every state-action pair to a value, which can become impractically large and inefficient in environments with large or continuous state spaces.

By employing a neural network, DQN can generalize across similar states and actions, allowing it to learn from fewer samples and handle larger, more complex environments effectively. This capability is particularly significant when dealing with problems that have a vast number of potential states, where maintaining a table for each would be computationally prohibitive.

While other aspects like computational power, learning rates, and policy enhancement are important in reinforcement learning, the distinctive improvement DQN brings is its method of representation through neural networks, allowing for scalable solutions beyond what Q-tables can manage.