Reinforcement Learning and Trustworthy Autonomy Jieliang Luo, Sam Green, Peter Feghali, George Legrady, and Çetin Kaya Koç Abstract Cyber-Physical Systems (CPS) possess physical and software inter- dependence and are typically designed by teams of mechanical, electrical, and software engineers. The interdisciplinary nature of CPS makes them difficult to design with safety guarantees. When autonomy is incorporated, design complexity and, especially, the difficulty of providing safety assurances are increased. Vision- based reinforcement learning is an increasingly popular family of machine learning algorithms that may be used to provide autonomy for CPS. Understanding how visual stimuli trigger various actions is critical for trustworthy autonomy. In this chapter we introduce reinforcement learning in the context of Microsoft’s AirSim drone simulator. Specifically, we guide the reader through the necessary steps for creating a drone simulation environment suitable for experimenting with vision- based reinforcement learning. We also explore how existing vision-oriented deep learning analysis methods may be applied toward safety verification in vision-based reinforcement learning applications. 1 Introduction Cyber-Physical Systems (CPS) are becoming increasingly powerful and complex. For example, standard passenger vehicles have millions of lines of code and tens of processors [1], and they are the product of years of planning and engineering J. Luo () · S. Green · P. Feghali · G. Legrady University of California Santa Barbara, Santa Barbara, CA, USA e-mail:
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[email protected] Ç. K. Koç Istinye˙ University, Istanbul,˙ Turkey Nanjing University of Aeronautics and Astronautics, Nanjing, China University of California Santa Barbara, Santa Barbara, CA, USA e-mail:
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