Feature-based methods for large scale dynamic programming
Machine Learning - Special issue on reinforcement learning
New linear program performance bounds for queueing networks
Journal of Optimization Theory and Applications - Special issue in honor of Yu-Chi Ho
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Neuro-Dynamic Programming
Value iteration and optimization of multiclass queueing networks
Queueing Systems: Theory and Applications
Near-Optimal Reinforcement Learning in Polynomial Time
Machine Learning
Performance Evaluation and Policy Selection in Multiclass Networks
Discrete Event Dynamic Systems
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
Solving factored MDPs with continuous and discrete variables
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A Price-Directed Approach to Stochastic Inventory/Routing
Operations Research
Efficient solution algorithms for factored MDPs
Journal of Artificial Intelligence Research
Approximate Dynamic Programming via a Smoothed Linear Program
Operations Research
Approximate Linear Programming for Average Cost MDPs
Mathematics of Operations Research
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We introduce a new algorithm based on linear programming for optimization of average-cost Markov decision processes (MDPs). The algorithm approximates the differential cost function of a perturbed MDP via a linear combination of basis functions. We establish a bound on the performance of the resulting policy that scales gracefully with the number of states without imposing the strong Lyapunov condition required by its counterpart in de Farias and Van Roy [de Farias, D. P., B. Van Roy. 2003. The linear programming approach to approximate dynamic programming. Oper. Res.51(6) 850--865]. We investigate implications of this result in the context of a queueing control problem.