Deep Q-Networks (DQN)

Deep Q-Networks (DQNs) are a type of reinforcement learning algorithm that combines Q-learning with deep neural networks. Instead of using a traditional Q-table to store Q-values (which represent the expected reward for taking a specific action in a specific state), DQNs use a neural network to approximate the Q-function. This allows DQNs to handle environments with large or continuous state spaces where a Q-table would be impractical. The neural network takes the state as input and outputs the Q-values for each possible action, enabling the agent to learn optimal policies through trial and error.