Deep Q-Network (DQN) is the first successful application of learning, both directly from raw visual inputs as humans do and in a wide variety of environments. It contains deep convolutional network without hand-designed features. DQN is actually no more than standard Q-learning bundled with stability and epsilon-greedy exploration. In any application, the first thing you … Continue reading Deep Q-Network in Reinforcement Learning