Deep Q-Networks

Deep Q-Networks (DQNs) are a type of machine learning model that helps computers learn to make decisions by combining reinforcement learning with deep neural networks. They enable an agent to understand how to act in complex environments by estimating the value of different actions through a neural network. The agent observes the environment, chooses actions to maximize rewards over time, and continually updates its understanding based on experience. This approach allows machines to learn strategies for tasks like playing games or controlling robots more effectively than traditional methods, especially in environments with high complexity or large state spaces.