Deep Q-Networks (DQNs): A Deep Reinforcement Learning Algorithm for Game Playing
Deep Q-Networks (DQNs) is a powerful deep reinforcement learning algorithm that has revolutionized the field of game playing artificial intelligence (AI). With the ability to learn how to take actions that maximize a reward in a given environment, DQNs have been used to develop AI agents capable of playing complex games such as Atari and Go. In this blog, we will explore the background and significance of DQNs in reinforcement learning, the architecture and working of DQNs, and how they are used to develop AI agents for game playing. We will also delve into the potential applications of DQNs beyond game playing, their limitations, and future developments in the field. So, let's dive in and explore the fascinating world of DQNs! Brief history and background of DQNs : Deep Q-Networks (DQNs) were introduced by Google DeepMind researchers in 2013 as a variant of Q-learning, a popular reinforcement learning algorithm. DQNs differ from traditional Q-learning in that they us...