Recent Advances and Applications of Optimal Control and Reinforcement Learning in Guidance and Navigation Systems

Dear Colleagues,

Optimal control approach has provided fruitful solutions in many engineering problems such as guidance, navigation and control systems for last few decades. It widened understanding of complex control systems and contributed to flourishing of modern control systems. However, it is considered as a theoretical designing method that results in complex actions which heavily relies on well-specified mathematical models.

On the other hand, reinforcement learning makes model-free predictions and produces actions from the data obtained by interacting with complex environments. Recently, the dramatic progress in reinforcement learning has opened tremendous opportunities in various fields of science and engineering. It is also expected to be able to play an important role in more challenging guidance, navigation, and control applications, including autonomous cars, robots and aerial systems that require agile dynamics.

However, considering its potential, current reinforcement learning is only at the starting point, and has many challenges to overcome. Among them, developing efficient ways of using big data and guaranteeing safe and reliable interaction with a complex and uncertain environment are the main issues. Since the efficiency, safety and reliability problems are also the core topics of the optimal control theory, combining the existing optimal control and reinforcement learning theories shows great possibility of compensating the weaknesses of each and wide acceptance for practical applications.

The aim of this Special Issue is to invite papers for recent advances in theory and applications of optimal control and reinforcement learning in guidance, navigation and control systems, as well as to exploit the novel results of the combination of optimal control and reinforcement learning technology for unmanned vehicles, robots, or any dynamics systems. Potential topics of the Special Issue include, but are not limited to, the following:

- emerging technology in optimal control and reinforcement learning

- novel applications of optimal control and reinforcement learning in guidance, navigation and control systems

- combination of optimal control and reinforcement learning technology

- reinforcement learning based path planning and tracking.

- reinforcement learning based modeling and parameter optimization

- supplementing method of reinforcement learning using optimal control theory

- structure and training method of deep reinforcement learning networks

Prof. Sungsu Park
Prof. Keeyoung Choi
Guest Editors

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