Distributed Differential Dynamic Programming Architectures for Large-Scale Multi-Agent Control

This paper proposes two decentralized multi-agent optimal control methods that combine the computational ef- ficiency and scalability of Differential Dynamic Programming (DDP) and the distributed nature of the Alternating Direction Method of Multipliers (ADMM). The first one, Nested Distributed DDP (ND-DDP), is a three-level architecture which employs ADMM for consensus, an augmented Lagrangian layer for local constraints and DDP as the local optimizer. The second one, Merged Distributed DDP (MD-DDP), is a two-level architecture that addresses both consensus and local constraints with ADMM, further reducing computational complexity. Both frameworks are fully decentralized since all computations are parallelizable among the agents and only local communication is necessary. Simulation results that scale up to thousands of cars and hun- dreds of drones demonstrate the effectiveness of the algorithms. Superior scalability to large-scale systems against other DDP and sequential quadratic programming methods is also illustrated. Finally, hardware experiments on a multi-robot platform verify the applicability of the methods. A video with all results is provided in the supplementary material.