Long-term planning, short-term adjustments

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.