Results 21 to 30 of about 30,788 (321)
Machine Learning and Knowledge Extraction, 2021 The two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) for solving partially observable Markov decision processes (POMDP) problems.
Xuanchen Xiang, Simon Foo, Huanyu Zang
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Experimental Design for Partially Observed Markov Decision Processes [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2018 39 pages, 3 ...
Thorbergsson, Leifur, Hooker, Giles
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Machine Learning and Knowledge Extraction, 2021 The first part of a two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) applications for solving partially observable Markov decision processes (POMDP) problems.
Xuanchen Xiang, Simon Foo
doaj +1 more source
Human-in-the-Loop Synthesis for Partially Observable Markov Decision Processes [PDF]
, 2018 We study planning problems where autonomous agents operate inside environments that are subject to uncertainties and not fully observable. Partially observable Markov decision processes (POMDPs) are a natural formal model to capture such problems ...
cassandra +20 more
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Structural Estimation of Partially Observable Markov Decision Processes
IEEE Transactions on Automatic Control, 2023 In many practical settings control decisions must be made under partial/imperfect information about the evolution of a relevant state variable. Partially Observable Markov Decision Processes (POMDPs) is a relatively well-developed framework for modeling and analyzing such problems.
Yanling Chang +3 more
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Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions [PDF]
, 2015 The focus of this paper is on solving multi-robot planning problems in continuous spaces with partial observability. Decentralized partially observable Markov decision processes (Dec-POMDPs) are general models for multi-robot coordination problems, but ...
Agha-mohammadi, Ali-akbar +3 more
core +7 more sources
IEEE Access, 2023 This work presents the method based on the Partially Observable Markov Decision Processes (POMDP) and consensus protocol. The main idea is to share the belief and reach the consensus on the belief state in order to improve local decision making.
Marijana Peti +2 more
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Entropy Maximization for Partially Observable Markov Decision Processes
IEEE Transactions on Automatic Control, 2022 14 pages, 10 figures.
Yagiz Savas +4 more
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Learning State-Variable Relationships in POMCP: A Framework for Mobile Robots
Frontiers in Robotics and AI, 2022 We address the problem of learning relationships on state variables in Partially Observable Markov Decision Processes (POMDPs) to improve planning performance.
Maddalena Zuccotto +4 more
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IEEE Access, 2018 In recent years, reinforcement learning (RL) has achieved remarkable success due to the growing adoption of deep learning techniques and the rapid growth of computing power.
Tuyen P. Le +2 more
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