What is Deep Q-Network (DQN)

Deep Q-Network (DQN) is a type of artificial intelligence (AI) algorithm that uses deep learning to solve complex problems. It is a reinforcement learning technique that is used to train agents in an environment. The agent learns how to maximize its reward by taking actions in the environment.

DQN is a type of neural network that combines reinforcement learning with deep learning. It is a powerful tool for solving complex problems that require decision making. It is used in many applications such as robotics, natural language processing, and computer vision.

DQN works by taking in a state of the environment and then predicting the best action to take based on that state. The agent then takes the action and receives a reward or penalty based on the outcome of the action. The agent then uses this reward to update its parameters and learns how to take better actions in the future.

DQN is used in many applications such as playing video games, robotics, and natural language processing. It is also used in autonomous driving, where it is used to detect objects on the road and make decisions about how to navigate the environment.

DQN is a powerful tool for solving complex problems. It is an important part of the development of artificial intelligence and machine learning. It is a powerful tool for creating agents that can learn to take the best actions in an environment.