Stochastic dynamic programming and the control of queueing systems
Stochastic dynamic programming and the control of queueing systems
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
The Linear Programming Approach to Approximate Dynamic Programming
Operations Research
On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming
Mathematics of Operations Research
Mathematics of Operations Research
Dynamic Programming and Optimal Control, Vol. II
Dynamic Programming and Optimal Control, Vol. II
Constraint relaxation in approximate linear programs
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Approximate Dynamic Programming via a Smoothed Linear Program
Operations Research
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We consider the linear programming approach to approximate dynamic programming with an average cost objective and a finite state space. Using a Lagrangian form of the linear program LP, the average cost error is shown to be a multiple of the best fit differential cost error. This result is analogous to previous error bounds for a discounted cost objective. Second, bounds are derived for average cost error and performance of the policy generated from the LP that involve the mixing time of the Markov decision process MDP under this policy or the optimal policy. These results improve on a previous performance bound involving mixing times.