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» How to Dynamically Merge Markov Decision Processes
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115
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NIPS
2000
15 years 5 months ago
APRICODD: Approximate Policy Construction Using Decision Diagrams
We propose a method of approximate dynamic programming for Markov decision processes (MDPs) using algebraic decision diagrams (ADDs). We produce near-optimal value functions and p...
Robert St-Aubin, Jesse Hoey, Craig Boutilier
142
Voted
JAIR
2002
120views more  JAIR 2002»
15 years 3 months ago
Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap
Hidden Markov models hmms and partially observable Markov decision processes pomdps provide useful tools for modeling dynamical systems. They are particularly useful for represent...
Hagit Shatkay, Leslie Pack Kaelbling
119
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IJCAI
2003
15 years 5 months ago
Multiple-Goal Reinforcement Learning with Modular Sarsa(0)
We present a new algorithm, GM-Sarsa(0), for finding approximate solutions to multiple-goal reinforcement learning problems that are modeled as composite Markov decision processe...
Nathan Sprague, Dana H. Ballard
108
Voted
AIPS
2006
15 years 5 months ago
Automated Planning Using Quantum Computation
This paper presents an adaptation of the standard quantum search technique to enable application within Dynamic Programming, in order to optimise a Markov Decision Process. This i...
Sanjeev Naguleswaran, Langford B. White, I. Fuss
131
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IJVR
2008
169views more  IJVR 2008»
15 years 3 months ago
Agent Architecture for a Real World Autonomous Virtual Guide: Interaction between the Decision and Perception Processes and Envi
Museums like marine aquariums are facing a difficult problem when trying to deliver information to their visitors. The exhibits they propose are dynamic by definition. Each may con...
Morgan Veyret, Eric Maisel, Jacques Tisseau