In order to overcome the limitation of traditional reinforcement learning techniques on the
restricted dimensionality of state and action spaces, the recent breakthroughs of deep
reinforcement learning (DRL) in Alpha Go and playing Atari set a good example in handling
large state and action spaces of complicated control problems. The DRL technique is
comprised of an offline deep neural network (DNN) construction phase and an online deep
Q-learning phase. In the offline phase, DNNs are utilized to derive the correlation between …