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In the context of reinforcement learning, traditional Q-tables are effective for environments with a small, discrete state and action space, allowing for the storage of state-action value pairs. However, when dealing with more complex environments that involve high-dimensional input spaces, like images or continuous state spaces, Q-tables become impractical due to their size and the requirement for extensive memory.

Deep Q-networks (DQNs) address this challenge by utilizing deep neural networks to approximate the Q-value function. In essence, DQNs enable the use of function approximation to generalize from a limited set of experiences and enable the agent to make better decisions when faced with states it has not encountered before. This ability to learn directly from high-dimensional sensory input, such as pixels in video games, is a significant advantage that allows reinforcement learning techniques to scale to more complex problems.

While policy networks and value estimation networks are also important components of reinforcement learning, they serve different purposes. Policy networks are typically used in policy gradient methods to directly parameterize policies, while value estimation networks may help in estimating state or action values but do not specifically focus on approximating the Q-values in the manner DQNs do. Supervised learning models do not apply directly to the reinforcement learning paradigm as