Coordinated Cooperative Distributed Decision-Making using Synchronization of Local Plans

Abstract

<p>Centralized decision-making for a Networked Control System (NCS) suffers from a high computational burden on the planning agent. Distributed agents, which compute cooperative decision-making, increase computational performance. In cooperative decision-making, agents locally plan for a subset of all agents. Due to only local system knowledge of the agents, these local plans are inconsistent with the local plans of other agents. This inconsistency leads to the infeasibility of plans. This article introduces an algorithm for synchronizing local plans for cooperative distributed decision-making. The algorithm enables parallel decision-making and achieves feasible decisions. The algorithm consists of two iterative steps: local planning and global synchronization. In the local planning step, the agents parallelly compute local decisions, referred to as plans. Subsequently, consistency of the local plans across agents is achieved using global synchronization. The globally synchronized plans act as reference decisions for the local planning step in the next iteration. In each iteration, the local planning guarantees locally feasible plans, while the synchronization guarantees globally consistent plans in that iteration. The parallel algorithm converges to globally feasible decisions if the coupling topology is feasible. We introduce requirements for the coupling topology to achieve convergence to globally feasible decisions and present the algorithm using a model predictive control example. Our evaluations with car-like robots show that globally feasible decisions are achieved.</p>

Progression Cognition Reinforcement Learning With Prioritized Experience for Multi-Vehicle Pursuit

“…In the existing MARL algorithms for MVP and AD, deep neural network parameters are shared among agents via centralized training with decentralized execution (CTDE), which significantly improves learning efficiency and experience utilization [28]- [30]. Many CTDE-based deep MARL methods achieve state-of-the-art performance on some tasks, such as group matching game and path finding [31]- [34]. Although CTDE can accelerate training [35], it has poor performance in complex and difficult tasks, such as Google Research Football [36] and MVP.…”