Add to ORKGEfficient representations and solutions for large decision problems with continuous and discrete variables are among the most important challenges faced by the designers of automated decision support systems. In this paper, we describe a novel hybrid factored Markov decision process (MDP) model that allows for a compact representation of these problems, and a new hybrid approximate linear programming (HALP) framework that permits their efficient solutions. The central idea of HALP is to approximate the optimal value function by a linear combination of basis functions and optimize its weights by linear programming
This paper addresses the problem of planning under uncertainty in large Markov Decision Processes (M...
A weakness of classical Markov decision processes (MDPs) is that they scale very poorly due to the f...
Hybrid (mixed discrete and continuous) state and action Markov Decision Processes (HSA-MDPs) provide...
Efficient representations and solutions for large decision problems with continuous and discrete var...
Efficient representations and solutions for large structured decision problems with continuous and d...
Hybrid approximate linear programming (HALP) has recently emerged as a promising approach to solvin...
Markov decision processes (MDPs) with discrete and continuous state and action components can be so...
Hybrid approximate linear programming (HALP) has recently emerged as a promising framework for solvi...
Hybrid approximate linear programming (HALP) has recently emerged as a promising framework for solvi...
Abstract Approximate linear programming (ALP) has emerged recently as one ofthe most promising metho...
Efficient representations and solutions for large structured decision problems with continuous and d...
I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, includ...
Although many real-world stochastic planning problems are more naturally formulated by hybrid models...
This paper investigates Factored Markov Decision Processes with Imprecise Probabilities (MDPIPs); th...
This paper addresses the problem of planning under uncertainty in large Markov Decision Processes (M...
This paper addresses the problem of planning under uncertainty in large Markov Decision Processes (M...
A weakness of classical Markov decision processes (MDPs) is that they scale very poorly due to the f...
Hybrid (mixed discrete and continuous) state and action Markov Decision Processes (HSA-MDPs) provide...
Efficient representations and solutions for large decision problems with continuous and discrete var...
Efficient representations and solutions for large structured decision problems with continuous and d...
Hybrid approximate linear programming (HALP) has recently emerged as a promising approach to solvin...
Markov decision processes (MDPs) with discrete and continuous state and action components can be so...
Hybrid approximate linear programming (HALP) has recently emerged as a promising framework for solvi...
Hybrid approximate linear programming (HALP) has recently emerged as a promising framework for solvi...
Abstract Approximate linear programming (ALP) has emerged recently as one ofthe most promising metho...
Efficient representations and solutions for large structured decision problems with continuous and d...
I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, includ...
Although many real-world stochastic planning problems are more naturally formulated by hybrid models...
This paper investigates Factored Markov Decision Processes with Imprecise Probabilities (MDPIPs); th...
This paper addresses the problem of planning under uncertainty in large Markov Decision Processes (M...
This paper addresses the problem of planning under uncertainty in large Markov Decision Processes (M...
A weakness of classical Markov decision processes (MDPs) is that they scale very poorly due to the f...
Hybrid (mixed discrete and continuous) state and action Markov Decision Processes (HSA-MDPs) provide...