Abstract. We examine scaling issues for a restricted class of compactly representable Markov decision process planning problems. For one stochastic mobile ...
Abstract: We examine scaling issues for a restricted class of compactly representable Markov decision process planning problems. For one stochastic mobile ...
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Jul 28, 2002 · We examine scaling issues for a restricted class of compactly representable Markov decision process planning problems. For one stochastic ...
J. Artif. Intell. Res. 2001. We show that for several variations of partially observable Markov decision processes, polynomial-time algorithms for finding ...
We examine scaling issues for a restricted class of compactly representable Markov decision process planning problems. ResearchGate Logo. Discover the world's ...
Abstract : Abstraction is a common method to compute lower bounds in classical planning, imposing an equivalence relation on the state space and deriving ...
Planning under uncertainty in large state–action spaces re- quires hierarchical abstraction for efficient computation. We introduce a new hierarchical planning ...
In this paper, we consider planning in stochastic shortest path (SSP) problems, a subclass of. Markov Decision Problems (MDP). We focus on.
A fundamental assumption of reinforcement learning in Markov decision processes. (MDPs) is that the relevant decision process is, in fact, Markov.
Recent research in decision theoretic planning has fo cussed on making the solution of M arkov decision pro cesses (MDPs) more feasible. We develop a family.