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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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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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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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Partially observable Markov decision processes with partially observable random discount factors [PDF]
, 2022 summary:This paper deals with a class of partially observable discounted Markov decision processes defined on Borel state and action spaces, under unbounded one-stage cost.
Martinez-Garcia, E. Everardo +2 more
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Wireless Body Area Network Control Policies for Energy-Efficient Health Monitoring
Sensors, 2021 Wireless body area networks (WBANs) have strong potential in the field of health monitoring. However, the energy consumption required for accurate monitoring determines the time between battery charges of the wearable sensors, which is a key performance ...
Yair Bar David +5 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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Applied Sciences, 2021 During the last decades, collaborative robots capable of operating out of their cages are widely used in industry to assist humans in mundane and harsh manufacturing tasks. Although such robots are inherently safe by design, they are commonly accompanied
Angeliki Zacharaki +2 more
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Model-Free Deep Recurrent Q-Network Reinforcement Learning for Quantum Circuit Architectures Design
Quantum Reports, 2022 Artificial intelligence (AI) technology leads to new insights into the manipulation of quantum systems in the Noisy Intermediate-Scale Quantum (NISQ) era.
Tomah Sogabe +6 more
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Multiple-Environment Markov Decision Processes [PDF]
, 2014 We introduce Multi-Environment Markov Decision Processes (MEMDPs) which are MDPs with a set of probabilistic transition functions. The goal in a MEMDP is to synthesize a single controller with guaranteed performances against all environments even though ...
Raskin, Jean-François, Sankur, Ocan
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