  We formulate offloading of computational tasks from a dynamic group of mobile
agents (e.g., cars) as decentralized decision making among autonomous agents.
We design an interaction mechanism that incentivizes such agents to align
private and system goals by balancing between competition and cooperation. In
the static case, the mechanism provably has Nash equilibria with optimal
resource allocation. In a dynamic environment, this mechanism's requirement of
complete information is impossible to achieve. For such environments, we
propose a novel multi-agent online learning algorithm that learns with partial,
delayed and noisy state information, thus greatly reducing information need.
Our algorithm is also capable of learning from long-term and sparse reward
signals with varying delay. Empirical results from the simulation of a V2X
application confirm that through learning, agents with the learning algorithm
significantly improve both system and individual performance, reducing up to
30% of offloading failure rate, communication overhead and load variation,
increasing computation resource utilization and fairness. Results also confirm
the algorithm's good convergence and generalization property in different
environments.
