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AIPS
2007
13 years 7 months ago
Learning to Plan Using Harmonic Analysis of Diffusion Models
This paper summarizes research on a new emerging framework for learning to plan using the Markov decision process model (MDP). In this paradigm, two approaches to learning to plan...
Sridhar Mahadevan, Sarah Osentoski, Jeffrey Johns,...
CVPR
2009
IEEE
1468views Computer Vision» more  CVPR 2009»
15 years 24 days ago
Hardware-Efficient Belief Propagation
Belief propagation (BP) is an effective algorithm for solving energy minimization problems in computer vision. However, it requires enormous memory, bandwidth, and computation beca...
Chao-Chung Cheng, Chia-Kai Liang, Homer H. Chen, L...
AAAI
2008
13 years 7 months ago
Towards Faster Planning with Continuous Resources in Stochastic Domains
Agents often have to construct plans that obey resource limits for continuous resources whose consumption can only be characterized by probability distributions. While Markov Deci...
Janusz Marecki, Milind Tambe
FLAIRS
2004
13 years 6 months ago
State Space Reduction For Hierarchical Reinforcement Learning
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
Mehran Asadi, Manfred Huber
EOR
2006
107views more  EOR 2006»
13 years 5 months ago
Longest path analysis in networks of queues: Dynamic scheduling problems
In this paper, we develop an acyclic network of queues for the design of a dynamic flow shop, where each service station represents a machine. The orders arrive at the input stati...
Amir Azaron, Hideki Katagiri, Kosuke Kato, Masatos...