Deep Q-Network, or DQN, is a method that approximates a state-value function in a Q-Learning framework with a neural network. It is commonly used in Atari Games, where it takes multiple game frames as input and produces state values for each available action as output.
How DQN Works
DQN works by taking multiple game frames as input and outputting state values for each available action. The Q-Network is used for this, and it is optimized toward a frozen target network that is periodically updated with the latest weights every k steps. Experience Replay is used in conjunction with DQN, where the episode steps are stored in memory for off-policy learning, and the samples are drawn at random. The replay memory helps to tackle autocorrelation that would occur from online learning, making the problem more like a supervised learning problem.
The Advantages of DQN
DQN has several advantages, including being able to learn from experience and not having to suffer from catastrophic forgetting, which occurs when new information is learned in such a way that old information is overwritten. Additionally, DQN can be used in various settings, making it popular for its versatility.
Real-life Applications of DQN
DQN is applicable to many areas of life, including self-driving cars, robotics, gaming, and finance. In self-driving cars, DQN can learn to deal with dangerous situations and make quick decisions. It can also be used for robotics, where it helps robots learn to navigate their environment and accomplish tasks. In gaming, DQN can optimize performance and improve the player's experience. Finally, it can be used in finance to make decisions about investments, and in this way, it can provide a competitive edge to companies.
All in all, DQN is a powerful method that uses neural networks to approximate a state-value function in a Q-Learning framework, improving stability and making it more like a supervised learning problem. The versatility of DQN makes it applicable in various fields of sense-making, artificial intelligence, and industry, further showing the relevance of the role that the use of DQN plays in many aspects of modern-day life.
