Formation Control using Simplified Reinforcement Learning for Multi-agent systems with State Delay

In this paper, a simplified reinforcement learning (RL) of identifier-actor-critic architecture is proposed to solve the formation problem of multiagent state-delay system. The dynamics of multi-agent systems includes the uncertainties and state delay, which is more practical in applications. In the multiagent system formation control, the system uncertainties are counteracted through the fuzzy logic system; the state delay is offset by applying a Lyapunov-Krasovskii functional. The updating laws of RL are derived from a simple equation, which is equivalent to the gradient of the HJB equation. Finally, a simulation example is given to demonstrate the satisfactory performance of the proposed method.