  The recent breakthroughs of deep reinforcement learning (DRL) technique in
Alpha Go and playing Atari have set a good example in handling large state and
actions spaces of complicated control problems. The DRL technique is comprised
of (i) an offline deep neural network (DNN) construction phase, which derives
the correlation between each state-action pair of the system and its value
function, and (ii) an online deep Q-learning phase, which adaptively derives
the optimal action and updates value estimates. In this paper, we first present
the general DRL framework, which can be widely utilized in many applications
with different optimization objectives. This is followed by the introduction of
three specific applications: the cloud computing resource allocation problem,
the residential smart grid task scheduling problem, and building HVAC system
optimal control problem. The effectiveness of the DRL technique in these three
cyber-physical applications have been validated. Finally, this paper
investigates the stochastic computing-based hardware implementations of the DRL
framework, which consumes a significant improvement in area efficiency and
power consumption compared with binary-based implementation counterparts.
