  Efforts in this paper seek to combine graph theory with adaptive dynamic
programming (ADP) as a reinforcement learning (RL) framework to determine
forward-in-time, real-time, approximate optimal controllers for distributed
multi-agent systems with uncertain nonlinear dynamics. A decentralized
continuous time-varying control strategy is proposed, using only local
communication feedback from two-hop neighbors on a communication topology that
has a spanning tree. An actor-critic-identifier architecture is proposed that
employs a nonlinear state derivative estimator to estimate the unknown dynamics
online and uses the estimate thus obtained for value function approximation.
