Reinforcement learning-based Optimized Multi-AgentFinite-time optimal synchronisation Control and its applicationto the Harmonic Oscillator

This paper investigates the finite-time optimal synchronisation problem for leader-follower multiagent systems and its application to the harmonic oscillator models. Neural networks are introduced to fit the nonlinear terms of multi-agent systems due to the existence of unknown dynamics. In our designed framework, by modelling each agent as a resonator, their interactions and environment can be shaped as a networked system. In order to achieve synchronised behaviour with neighbouring agents in the network, an actor-critic reinforcement learning algorithm is used to train each agent to learn the optimal policy while minimising energy consumption and maximising the overall performance of the system. Besides, a collaborative control strategy is designed such that it ensures that all agents work together to achieve the desired state in a finite amount of time. Finally, the validity of the theoretical method is proven by the Lyapunov stability theory and numerical simulation.