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Observable Markov Decision Process
"... (Article begins on next page) The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. ..."
Abstract
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(Article begins on next page) The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters.
Partially Observable Markov Decision Processes
"... Partially Observable Markov Decision Processes (POMDPs) are attractive for dialogue management because they are made to deal with noise and partial information. This paper addresses the problem of using them in a practical development cycle. We apply factored POMDP models to three applications. We e ..."
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Partially Observable Markov Decision Processes (POMDPs) are attractive for dialogue management because they are made to deal with noise and partial information. This paper addresses the problem of using them in a practical development cycle. We apply factored POMDP models to three applications. We
Algorithms for Partially Observable Markov Decision Processes
- HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY
, 2001
"... Partially Observable Markov Decision Process (POMDP) is a general sequential decision-making model where the effects of actions are... ..."
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Cited by 21 (1 self)
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Partially Observable Markov Decision Process (POMDP) is a general sequential decision-making model where the effects of actions are...
Partially-Observable Markov Decision Processes
"... Partially-Observable Markov Decision Processes (POMDPs) are typically solved by finding an approximate global solution to a corresponding belief-MDP. In this paper, we offer a new method to solve POMDPs with continuous state, action and observation spaces. Since such domains have an inherent notion ..."
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Partially-Observable Markov Decision Processes (POMDPs) are typically solved by finding an approximate global solution to a corresponding belief-MDP. In this paper, we offer a new method to solve POMDPs with continuous state, action and observation spaces. Since such domains have an inherent notion
♯-acyclic Partially Observable Markov Decision Processes ⋆
, 2013
"... Abstract. The value 1 problem is a natural decision problem in algorithmic game theory. For partially observable Markov decision processes with reachability objective, this problem is defined as follows: are there observational strategies that achieve the reachability objective with probability arbi ..."
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Abstract. The value 1 problem is a natural decision problem in algorithmic game theory. For partially observable Markov decision processes with reachability objective, this problem is defined as follows: are there observational strategies that achieve the reachability objective with probability
Transition Entropy in Partially Observable Markov Decision Processes
- Proceedings of the 9th International Conference on Intelligent Autonomous Systems (IAS-9)
, 2006
"... This paper proposes a new heuristic algorithm suitable for real-time applications using partially observable Markov decision processes (POMDP). The algorithm is based in a reward shaping strategy which includes entropy information in the reward structure of a fully observable Markov decision process ..."
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Cited by 7 (3 self)
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This paper proposes a new heuristic algorithm suitable for real-time applications using partially observable Markov decision processes (POMDP). The algorithm is based in a reward shaping strategy which includes entropy information in the reward structure of a fully observable Markov decision
Partially Observable Markov Decision Processes
"... We parallelize the Point-Based Value Iteration (PBVI) algo-rithm, which approximates the solution to Partially Observ-able Markov Decision Processes (POMDPs), using a Graph-ics Processing Unit (GPU). We detail additional optimiza-tions, such as leveraging the bounded size of non-zero values over all ..."
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We parallelize the Point-Based Value Iteration (PBVI) algo-rithm, which approximates the solution to Partially Observ-able Markov Decision Processes (POMDPs), using a Graph-ics Processing Unit (GPU). We detail additional optimiza-tions, such as leveraging the bounded size of non-zero values over
Partially Observable Markov Decision Processes
"... A nonparametric approach for policy learn-ing for POMDPs is proposed. The approach represents distributions over the states, ob-servations, and actions as embeddings in feature spaces, which are reproducing ker-nel Hilbert spaces. Distributions over states given the observations are obtained by ap-p ..."
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A nonparametric approach for policy learn-ing for POMDPs is proposed. The approach represents distributions over the states, ob-servations, and actions as embeddings in feature spaces, which are reproducing ker-nel Hilbert spaces. Distributions over states given the observations are obtained by ap
Results 1 - 10
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