title: Long-term planning, short-term adjustments
abstract: 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 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.
  We achieve this by modeling both short-term and long-term prediction models.
  The short-term prediction model represents the estimation of the system dynamic
  while the long-term prediction model represents the Q-value. The case studies
  demonstrate that our proposed method can achieve short-term and long-term goals
  without complex reward functions.
authors: Hamed Khorasgani Chi Zhang Chetan Gupta Susumu Serita
hIndex: Hamed Khorasgani 11