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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, 2021 Most real-world problems are essentially partially observable, and the environmental model is unknown. Therefore, there is a significant need for reinforcement learning approaches to solve them, where the agent perceives the state of the environment ...
Mehmet Haklidir, Hakan Temeltas
doaj +1 more source
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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Quantum partially observable Markov decision processes [PDF]
Physical Review A, 2014 We present quantum observable Markov decision processes (QOMDPs), the quantum analogs of partially observable Markov decision processes (POMDPs). In a QOMDP, an agent is acting in a world where the state is represented as a quantum state and the agent can choose a superoperator to apply. This is similar to the POMDP belief state, which is a probability
Barry, Jennifer +2 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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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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Forward and Backward Bellman Equations Improve the Efficiency of the EM Algorithm for DEC-POMDP
Entropy, 2021 Decentralized partially observable Markov decision process (DEC-POMDP) models sequential decision making problems by a team of agents. Since the planning of DEC-POMDP can be interpreted as the maximum likelihood estimation for the latent variable model ...
Takehiro Tottori, Tetsuya J. Kobayashi
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Partially observable Markov decision processes with partially observable random discount factors
Kybernetika, 2023 A discrete time Markov decision process with Borel state and action process and with discounted, unbounded one-step costs is considered. The value of the discount factor is Markov random and independent of the state process. The transition functions for both processes are explicitly given.
Martinez-Garcia, E. Everardo +2 more
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On Anderson Acceleration for Partially Observable Markov Decision Processes [PDF]
2021 60th IEEE Conference on Decision and Control (CDC), 2021 This paper proposes an accelerated method for approximately solving partially observable Markov decision process (POMDP) problems offline. Our method carefully combines two existing tools: Anderson acceleration (AA) and the fast informed bound (FIB) method.
Ermis, Melike +2 more
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Underwater chemical plume tracing based on partially observable Markov decision process
International Journal of Advanced Robotic Systems, 2019 Chemical plume tracing based on autonomous underwater vehicle uses chemical as a guidance to navigate and search in the unknown environments. To solve the key issue of tracing and locating the source, this article proposes a path-planning strategy based ...
Jiu Hai-Feng +3 more
doaj +1 more source