LAUSR

In this article, we investigate a novel reinforcement-learning (RL)-based scheme to address the optimal leader–follower consensus issue for constrained-input continuous-time multiagent systems. First, as for input-constrained problems, the fundamental smooth assumption is not satisfied for the value function. To deal with this problem, we employ the method of vanishing viscosity solutions to relax this smooth assumption to a continuity assumption for the value function, which broadens the scope of RL applications. Second, the control cost functions take a more general form which guarantees the continuity of the optimal control policy instead of the specific integrand form used in previous input-constrained RL-based schemes. Based on these results, we introduce a novel identifier–critic–actor structure to extend the conventional critic–actor RL framework into a distributed model-free one, where the learning of the identifier, critic, and actor is online and simultaneous. We provide the simulation examples to validate the effectiveness of the proposed scheme.