Provable Reinforcement Learning for Networked Control Systems with Stochastic Packet Disordering

This paper formulates a stochastic optimal control problem for linear networked control systems featuring stochastic packet disordering with a unique stabilizing solution certified. The problem is solved by proposing reinforcement learning algorithms. A measurement method is first presented to deal with PD and calculate the newest control input. The NCSs with stochastic PD are modeled as stochastic NCSs. Then, given a cost function, a modified algebraic Riccati equation is derived within the formulation. We propose offline policy iteration and value iteration algorithms to solve the MARE associated with provable convergence. These two algorithms require knowledge of NCS dynamics and PD probabilities. To release that, we further design online model-free off-policy and Q-learning algorithms with an online estimation method for PD probability. Both model-free algorithms solve the optimal control problem using real-time system states, control inputs, and PD probability estimates. Simulation results verify the proposed formulation and algorithms at last.