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
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Decision-making models on perceptual uncertainty with distributional reinforcement learning
Green Energy and Intelligent Transportation, 2023 Decision-making for autonomous vehicles in the presence of obstacle occlusions is difficult because the lack of accurate information affects the judgment.
Shuyuan Xu +4 more
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Partially Observable Markov Decision Processes [PDF]
, 2012 For reinforcement learning in environments in which an agent has access to a reliable state signal, methods based on the Markov decision process (MDP) have had many successes. In many problem domains, however, an agent suffers from limited sensing capabilities that preclude it from recovering a Markovian state signal from its perceptions. Extending the
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Blackwell optimality in Markov decision processes with partial observation [PDF]
The Annals of Statistics, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rosenberg, Dinah +2 more
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Cost-Bounded Active Classification Using Partially Observable Markov Decision Processes [PDF]
, 2018 Active classification, i.e., the sequential decision-making process aimed at data acquisition for classification purposes, arises naturally in many applications, including medical diagnosis, intrusion detection, and object tracking.
Ahmadi, Mohamadreza +3 more
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Technical Report: Distribution Temporal Logic: Combining Correctness with Quality of Estimation [PDF]
, 2013 We present a new temporal logic called Distribution Temporal Logic (DTL) defined over predicates of belief states and hidden states of partially observable systems.
Belta, Calin +2 more
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International Journal of Advanced Robotic Systems, 2018 In this article, we study the ground moving target tracking problem for a fixed-wing unmanned aerial vehicle equipped with a radar. This problem is formulated in a partially observable Markov process framework, which contains the following two parts: in ...
Yunyun Zhao +3 more
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