Lyapunov-Based Reinforcement Learning State Estimator
This addresses the problem of robust state estimation for nonlinear systems, which is incremental as it builds on existing methods with a new reinforcement learning approach.
The paper tackles the state estimation problem for nonlinear stochastic discrete-time systems by combining Lyapunov's method with deep reinforcement learning, resulting in a state estimator that shows advantage in estimate convergence under uncertainties like covariance shift and missing measurements in simulations.
In this paper, we consider the state estimation problem for nonlinear stochastic discrete-time systems. We combine Lyapunov's method in control theory and deep reinforcement learning to design the state estimator. We theoretically prove the convergence of the bounded estimate error solely using the data simulated from the model. An actor-critic reinforcement learning algorithm is proposed to learn the state estimator approximated by a deep neural network. The convergence of the algorithm is analysed. The proposed Lyapunov-based reinforcement learning state estimator is compared with a number of existing nonlinear filtering methods through Monte Carlo simulations, showing its advantage in terms of estimate convergence even under some system uncertainties such as covariance shift in system noise and randomly missing measurements. To the best of our knowledge, this is the first reinforcement learning based nonlinear state estimator with bounded estimate error performance guarantee.