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Maximum likelihood from incomplete data via the EM algorithm

by A. P. Dempster, N. M. Laird, D. B. Rubin - JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B , 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
Abstract - Cited by 11972 (17 self) - Add to MetaCart
A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value

Point-based value iteration: An anytime algorithm for POMDPs

by Joelle Pineau, Geoff Gordon, Sebastian Thrun , 2003
"... This paper introduces the Point-Based Value Iteration (PBVI) algorithm for POMDP planning. PBVI approximates an exact value iteration solution by selecting a small set of representative belief points, and planning for those only. By using stochastic trajectories to choose belief points, and by ..."
Abstract - Cited by 346 (25 self) - Add to MetaCart
This paper introduces the Point-Based Value Iteration (PBVI) algorithm for POMDP planning. PBVI approximates an exact value iteration solution by selecting a small set of representative belief points, and planning for those only. By using stochastic trajectories to choose belief points

Value iteration

by Krishnendu Chatterjee, Thomas A. Henzinger - 25 Years of Model Checking , 2008
"... Abstract. We survey value iteration algorithms on graphs. Such algo-rithms can be used for determining the existence of certain paths (model checking), the existence of certain strategies (game solving), and the probabilities of certain events (performance analysis). We classify the al-gorithms acco ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
Abstract. We survey value iteration algorithms on graphs. Such algo-rithms can be used for determining the existence of certain paths (model checking), the existence of certain strategies (game solving), and the probabilities of certain events (performance analysis). We classify the al

Value Iteration for

by Continuous-state Pomdps, Josep M. Porta, Matthijs T. J. Spaan, Nikos Vlassis
"... We present a value iteration algorithm for learning to act in Partially Observable Markov Decision Processes (POMDPs) with continuous state spaces. Mainstream POMDP research focuses on the discrete case and this complicates its application to, e.g., robotic problems that are naturally modeled using ..."
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We present a value iteration algorithm for learning to act in Partially Observable Markov Decision Processes (POMDPs) with continuous state spaces. Mainstream POMDP research focuses on the discrete case and this complicates its application to, e.g., robotic problems that are naturally modeled using

Heuristic search value iteration for pomdps

by Trey Smith, Reid Simmons - In UAI , 2004
"... We present a novel POMDP planning algorithm called heuristic search value iteration (HSVI). HSVI is an anytime algorithm that returns a policy and a provable bound on its regret with respect to the optimal policy. HSVI gets its power by combining two well-known tech-niques: attention-focusing search ..."
Abstract - Cited by 137 (3 self) - Add to MetaCart
We present a novel POMDP planning algorithm called heuristic search value iteration (HSVI). HSVI is an anytime algorithm that returns a policy and a provable bound on its regret with respect to the optimal policy. HSVI gets its power by combining two well-known tech-niques: attention

Factored value iteration converges

by István Szita, András Lőrincz - Acta Cyb
"... Abstract. In this paper we propose a novel algorithm, factored value iteration (FVI), for the approximate solution of factored Markov decision processes (fMDPs). The traditional approximate value iteration algorithm is modified in two ways. For one, the least-squares projection operator is modified ..."
Abstract - Cited by 11 (2 self) - Add to MetaCart
Abstract. In this paper we propose a novel algorithm, factored value iteration (FVI), for the approximate solution of factored Markov decision processes (fMDPs). The traditional approximate value iteration algorithm is modified in two ways. For one, the least-squares projection operator is modified

Horizon-based Value Iteration

by Peng Zang, Arya Irani, Charles Isbell
"... We present a horizon-based value iteration algorithm called Re-verse Value Iteration (RVI). Empirical results on a variety of do-mains, both synthetic and real, show RVI often yields speedups of several orders of magnitude. RVI does this by ordering backups by horizons, with preference given to clos ..."
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We present a horizon-based value iteration algorithm called Re-verse Value Iteration (RVI). Empirical results on a variety of do-mains, both synthetic and real, show RVI often yields speedups of several orders of magnitude. RVI does this by ordering backups by horizons, with preference given

Perseus: Randomized point-based value iteration for POMDPs

by Matthijs T. J. Spaan, Nikos Vlassis - Journal of Artificial Intelligence Research , 2005
"... Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Point-based approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent’s belief space. We present a ra ..."
Abstract - Cited by 204 (17 self) - Add to MetaCart
randomized point-based value iteration algorithm called Perseus. The algorithm performs approximate value backup stages, ensuring that in each backup stage the value of each point in the belief set is improved; the key observation is that a single backup may improve the value of many belief points. Contrary

Value Iteration Networks

by Aviv Tamar , Yi Wu , Garrett Thomas , Sergey Levine , Pieter Abbeel
"... Abstract We introduce the value iteration network (VIN): a fully differentiable neural network with a 'planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key ..."
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Abstract We introduce the value iteration network (VIN): a fully differentiable neural network with a 'planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning

Topological Value Iteration Algorithms

by Peng Dai, Daniel S. Weld, Judy Goldsmith
"... Value iteration is a powerful yet inefficient algorithm for Markov decision processes (MDPs) because it puts the majority of its effort into backing up the entire state space, which turns out to be unnecessary in many cases. In order to overcome this problem, many approaches have been proposed. Amon ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
Value iteration is a powerful yet inefficient algorithm for Markov decision processes (MDPs) because it puts the majority of its effort into backing up the entire state space, which turns out to be unnecessary in many cases. In order to overcome this problem, many approaches have been proposed
Results 1 - 10 of 8,371