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Vehicular edge computing has recently been proposed to supportcomputation-intensive applications in Intelligent Transportation Systems (ITS)such as self-driving cars and augmented reality. Despite progress in this area,significant challenges remain to efficiently allocate limited computationresources to a range of time-critical ITS tasks. To this end, the current paperdevelops a new task assignment scheme for vehicles in a highway. Because of thehigh speed of vehicles and the limited communication range of road side units(RSUs), the computation tasks of participating vehicles are to be dynamicallymigrated across multiple servers. We formulate a binary nonlinear programming(BNLP) problem of assigning computation tasks from vehicles to RSUs and amacrocell base station. To deal with the potentially large size of theformulated optimization problem, we develop a distributed multi-agentregret-matching learning algorithm. Based on the regret minimization principle,the proposed algorithm employs a forgetting method that allows the learningprocess to quickly adapt to and effectively handle the high mobility feature ofvehicle networks. We theoretically prove that it converges to the correlatedequilibrium solutions of the considered BNLP problem. Simulation results withpractical parameter settings show that the proposed algorithm offers the lowesttotal delay and cost of processing tasks, as well as utility fairness amongagents. Importantly, our algorithm converges much faster than existing methodsas the problem size grows, demonstrating its clear advantage in large-scalevehicular networks.

Multi-Agent Task Assignment in Vehicular Edge Computing: A Regret-Matching Learning-Based Approach