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» Online Planning Algorithms for POMDPs
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ICRA
2005
IEEE
118views Robotics» more  ICRA 2005»
15 years 5 months ago
Planning with Continuous Actions in Partially Observable Environments
Abstract— We present a simple randomized POMDP algorithm for planning with continuous actions in partially observable environments. Our algorithm operates on a set of reachable b...
Matthijs T. J. Spaan, Nikos A. Vlassis
AAAI
2010
15 years 1 months ago
Trial-Based Dynamic Programming for Multi-Agent Planning
Trial-based approaches offer an efficient way to solve singleagent MDPs and POMDPs. These approaches allow agents to focus their computations on regions of the environment they en...
Feng Wu, Shlomo Zilberstein, Xiaoping Chen
AAAI
2011
13 years 11 months ago
An Online Spectral Learning Algorithm for Partially Observable Nonlinear Dynamical Systems
Recently, a number of researchers have proposed spectral algorithms for learning models of dynamical systems—for example, Hidden Markov Models (HMMs), Partially Observable Marko...
Byron Boots, Geoffrey J. Gordon
STOC
1991
ACM
127views Algorithms» more  STOC 1991»
15 years 3 months ago
Lower Bounds for Randomized k-Server and Motion Planning Algorithms
In this paper, we prove lower bounds on the competitive ratio of randomized algorithms for two on-line problems: the k-server problem, suggested by [MMS], and an on-line motion-pl...
Howard J. Karloff, Yuval Rabani, Yiftach Ravid
ATAL
2008
Springer
15 years 1 months ago
Value-based observation compression for DEC-POMDPs
Representing agent policies compactly is essential for improving the scalability of multi-agent planning algorithms. In this paper, we focus on developing a pruning technique that...
Alan Carlin, Shlomo Zilberstein