Optimal Control for Multi-agent Systems Using Off-Policy Reinforcement Learning

Abstract

To achieve the consensus for discrete-time multi-agent systems, an optimal control policy is designed based on off-policy reinforcement learning. By utilizing centralized learning and decentralized execution, we first define a centralized and shared value function. Then, a value iteration adaptive dynamic programming method is proposed to approach the solution of the Bellman optimality equation with convergence analysis. Furthermore, the actor-critic structure is given for the implementation purpose, where one single-critic network is given to approach the optimal centralized value function, and multi-actor networks are decentralized based on the local observation from the neighbors to obtain the optimal policy for each agent. Finally, the proposed algorithm is verified in a leader-follower consensus case.