Ph.D. Defense

Nikolaos Marios Kokolakis

(Advisor: Prof. Kyriakos Vamvoudakis)

"Fixed-Time Reinforcement Learning-based Control for Safe Autonomy"

Wednesday, May 8 

2:00 p.m.

Montgomery Knight 317

Abstract

Exploiting the benefits of learning can enhance the performance and ensure the safety of autonomous systems in complex and unknown environments. Nonetheless, existing safe learning architectures lack finite time convergence guarantees, rendering these algorithms impractical for real-world applications. In this dissertation, we enable safe autonomy by endowing autonomous systems with safety-critical control frameworks predicated on online reinforcement learning mechanisms with fixed-time convergence guarantees. Specifically, we develop a safe pursuit-evasion game for enabling finite-time capture, optimal performance, and adaptation to an unknown cluttered environment. Then, we leverage ideas from behavioral game theory to construct a learning-based evader assignment algorithm to address the problem of multiple bounded rational pursuers against multiple bounded rational evaders, wherein the assignment is performed based on the agent rationality level. Subsequently, we design an online reinforcement learning architecture with fixed-time convergence guarantees to address the optimal fixed-time stabilization problem. Finally, we address a safety-critical control problem using reachability analysis and design an online reinforcement learning-based mechanism for learning the solution to the safety-critical control problem in a fixed time.

Committee

• Prof. Kyriakos G. Vamvoudakis – School of Aerospace Engineering (advisor)
• Prof. Wassim M. Haddad – School of Aerospace Engineering
• Prof. Yongxin Chen – School of Aerospace Engineering
• Prof. Chaouki T. Abdallah – School of Electrical & Computer Engineering
• Prof. Claire J. Tomlin – Department of Electrical Engineering & Computer Sciences, UC Berkeley