Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
From Perturbation Analysis to Markov Decision Processes and Reinforcement Learning
Discrete Event Dynamic Systems
The Linear Programming Approach to Approximate Dynamic Programming
Operations Research
Stochastic Learning and Optimization: A Sensitivity-Based Approach (International Series on Discrete Event Dynamic Systems)
Introduction to Discrete Event Systems
Introduction to Discrete Event Systems
A Numerical Method for Solving Singular Stochastic Control Problems
Operations Research
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Markov Chains and Stochastic Stability
Markov Chains and Stochastic Stability
Policy iteration for customer-average performance optimization of closed queueing systems
Automatica (Journal of IFAC)
| Hi-index | 0.00 |
The standard approach to stochastic control is dynamic programming. In this paper, we introduce an alternative approach based on direct comparison of the performance of any two policies. This is achieved by modeling the state process as a continuous-time and continuous-state Markov process and applying the same ideas as for the discrete-time and discrete-state case. This approach is simple and intuitively clear; it applies to different problems with, finite and infinite horizons, discounted and long-run-average performance, continuous and jump diffusions, in the same way. Discounting is not needed when dealing with long-run average performance. The approach provides a unified framework for stochastic control and other optimization theory and methodologies, including Markov decision processes, perturbation analysis, and reinforcement learning.