  Deep reinforcement learning (RL) algorithms can learn complex policies to
optimize agent operation over time. RL algorithms have shown promising results
in solving complicated problems in recent years. However, their application on
real-world physical systems remains limited. Despite the advancements in RL
algorithms, the industries often prefer traditional control strategies.
Traditional methods are simple, computationally efficient and easy to adjust.
In this paper, we first propose a new Q-learning algorithm for continuous
action space, which can bridge the control and RL algorithms and bring us the
best of both worlds. Our method can learn complex policies to achieve long-term
goals and at the same time it can be easily adjusted to address short-term
requirements without retraining. Next, we present an approximation of our
algorithm which can be applied to address short-term requirements of any
pre-trained RL algorithm. The case studies demonstrate that both our proposed
method as well as its practical approximation can achieve short-term and
long-term goals without complex reward functions.
