Q-learning is a type of reinforcement learning that uses a mathematical formula called the Q-function to determine the best action to take in a given situation. The Q-function calculates the expected future reward for each possible action, and the machine learns to choose the action with the highest expected reward.
By combining these two techniques, DQN can learn to make complex decisions in real-time, even in environments with high-dimensional and continuous state spaces. This has led to impressive results in various tasks, such as playing video games, navigating mazes, and controlling robots.
The potential applications of DQN are vast, from improving autonomous vehicles and robotics to optimizing energy management and supply chain operations. As AI continues to advance, DQN will undoubtedly play a significant role in shaping the future of intelligent machines.
