Numerical methods for stochastic control problems in continuous time
Numerical methods for stochastic control problems in continuous time
Feature-based methods for large scale dynamic programming
Machine Learning - Special issue on reinforcement learning
Dynamic Programming and Optimal Control, Two Volume Set
Dynamic Programming and Optimal Control, Two Volume Set
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
From Perturbation Analysis to Markov Decision Processes and Reinforcement Learning
Discrete Event Dynamic Systems
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
It is well known that stochastic control systems can be viewed as Markov decision processes (MDPs) with continuous state spaces. In this paper, we propose to apply the policy iteration approach in MDPs to the optimal control problem of stochastic systems. We first provide an optimality equation based on performance potentials and develop a policy iteration procedure. Then we apply policy iteration to the jump linear quadratic problem and obtain the coupled Riccati equations for their optimal solutions. The approach is applicable to linear as well as nonlinear systems and can be implemented on-line on real world systems without identifying all the system structure and parameters.