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UAI
2000
15 years 3 months ago
PEGASUS: A policy search method for large MDPs and POMDPs
We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP), given a mo...
Andrew Y. Ng, Michael I. Jordan
ENTCS
2006
134views more  ENTCS 2006»
15 years 1 months ago
Partial Order Reduction for Probabilistic Branching Time
In the past, partial order reduction has been used successfully to combat the state explosion problem in the context of model checking for non-probabilistic systems. For both line...
Christel Baier, Pedro R. D'Argenio, Marcus Grö...
CORR
2010
Springer
105views Education» more  CORR 2010»
15 years 7 days ago
Optimism in Reinforcement Learning Based on Kullback-Leibler Divergence
We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic strategies. Optimism is usually implemented by carryin...
Sarah Filippi, Olivier Cappé, Aurelien Gari...
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ECCV
2004
Springer
16 years 3 months ago
An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets
Abstract. We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or be...
Zia Khan, Tucker R. Balch, Frank Dellaert
STOC
1989
ACM
99views Algorithms» more  STOC 1989»
15 years 5 months ago
A Random Polynomial Time Algorithm for Approximating the Volume of Convex Bodies
We present a randomised polynomial time algorithm for approximating the volume of a convex body K in n-dimensional Euclidean space. The proof of correctness of the algorithm relie...
Martin E. Dyer, Alan M. Frieze, Ravi Kannan