A Deep Q-Network (DQN) is a reinforcement learning algorithm that combines Q-learning with deep neural networks to enable agents to learn optimal actions in complex environments. Traditional Q-learning uses a table to store Q-values (estimates of expected rewards for actions in specific states), but this becomes impractical in environments with large state spaces, such as video games or robotics. DQN replaces this table with a neural network that approximates the Q-value function, allowing it to generalize across states and handle high-dimensional inputs like images. Key innovations in DQN include experience replay and a target network, which stabilize training and improve sample efficiency. For example, in an Atari game, the network might take raw pixel frames as input and output Q-values for each possible action (e.g., moving left or right).
DQN addresses two major challenges in reinforcement learning: correlated data and moving targets. Experience replay stores the agent’s experiences (state, action, reward, next state) in a buffer and randomly samples mini-batches during training. This breaks correlations between consecutive updates, which could otherwise destabilize learning. The target network is a separate neural network used to compute Q-value targets during training. By updating the target network periodically (e.g., every 1,000 steps) instead of continuously, DQN reduces the risk of feedback loops where the network chases its own changing predictions. For instance, when training an agent to navigate a maze, the target network ensures that the Q-values used for error calculation remain consistent over short periods, leading to more stable updates.
Implementing DQN requires careful design choices. The neural network architecture often includes convolutional layers for image-based inputs and fully connected layers for decision-making. Hyperparameters like the replay buffer size, learning rate, and target network update frequency significantly impact performance. A common pitfall is overestimating Q-values, which can be mitigated with techniques like Double DQN. While DQN excels in discrete action spaces (e.g., game controls), it struggles with continuous actions (e.g., robot arm movements), where algorithms like DDPG are more suitable. Developers can use frameworks like TensorFlow or PyTorch to build DQN models, but training remains computationally intensive, often requiring GPUs. For example, a DQN-based warehouse robot might learn to avoid obstacles by processing lidar data, but fine-tuning the network and reward function would be critical for reliable performance.