Adaptive Optimization Framework for Control of Multi-Agent Systems

The main focus of this work is an optimization-based framework for control of multi-agent systems that synthesizes actions steering a given system towards a specified state. The primary motivation for the research presented is a fascination with birds, which save energy on long-distance flights via forming a V-shape. We ask the following question: Are V-formations a result of solving an optimization problem and can this concept be utilized in multi-agent systems, particularly in drones swarms, to increase their safety and resilience? We demonstrate that our framework can be applied to any system modeled as a controllable Markov decision process with a cost (reward) function. A key feature of the procedure we propose is its automatic adaptation to the performance of optimization towards a given global objective. Combining model-predictive control and ideas from sequential Monte-Carlo methods, we introduce a performance-based adaptive horizon and implicitly build a Lyapunov function that guarantees convergence. We use statistical model-checking to verify the algorithm and assess its reliability