A model for reasoning about persistence and causation
Computational Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Linear Optimization
Introduction to Linear Optimization
Neuro-Dynamic Programming
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
Dynamic programming for structured continuous Markov decision problems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Solving factored MDPs with continuous and discrete variables
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Efficient solution algorithms for factored MDPs
Journal of Artificial Intelligence Research
Max-norm projections for factored MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Exploiting structure in policy construction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Probabilistic inference for solving discrete and continuous state Markov Decision Processes
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning basis functions in hybrid domains
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Solving factored MDPs with hybrid state and action variables
Journal of Artificial Intelligence Research
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Hybrid approximate linear programming (HALP) has recently emerged as a promising framework for solving large factored Markov decision processes (MDPs) with discrete and continuous state and action variables. Our work addresses its major computational bottleneck - constraint satisfaction in large structured domains of discrete and continuous variables. We analyze this problem and propose a novelMarkov chainMonte Carlo (MCMC) method for finding the most violated constraint of a relaxed HALP. This method does not require the discretization of continuous variables, searches the space of constraints intelligently based on the structure of factored MDPs, and its space complexity is linear in the number of variables. We test the method on a set of large control problems and demonstrate improvements over alternative approaches.