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Markov Decision Processes [PDF]
Journal of the Royal Statistical Society. Series A (Statistics in Society), 1994 9. Markov Decision Processes. By D. J. White. ISBN 0 471 93627 8. Wiley, Chichester, 1992. 224 pp. £29.95.
Martin L. Puterman
+8 more sources
The Convergence of a Cooperation Markov Decision Process System [PDF]
Entropy, 2020 In a general Markov decision progress system, only one agent’s learning evolution is considered. However, considering the learning evolution of a single agent in many problems has some limitations, more and more applications involve multi-agent.
Xiaoling Mo, Daoyun Xu, Zufeng Fu
doaj +2 more sources
Improving RED algorithm congestion control by using the Markov decision process [PDF]
Scientific Reports, 2022 Congestion control plays an essential role on the internet to manage overload, which affects data transmission performance. The random early detection (RED) algorithm belongs to active queue management (AQM), which is used to manage internet traffic. The
Amar A. Mahawish, Hassan J. Hassan
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Markov Abstractions for PAC Reinforcement Learning in Non-Markov Decision Processes [PDF]
arXiv, 2022 Our work aims at developing reinforcement learning algorithms that do not rely on the Markov assumption. We consider the class of Non-Markov Decision Processes where histories can be abstracted into a finite set of states while preserving the dynamics. We call it a Markov abstraction since it induces a Markov Decision Process over a set of states that ...
De Giacomo, G, Licks, Gp, Ronca, A
arxiv +4 more sources
Intelligent Sensing in Dynamic Environments Using Markov Decision Process [PDF]
Sensors, 2011 In a network of low-powered wireless sensors, it is essential to capture as many environmental events as possible while still preserving the battery life of the sensor node. This paper focuses on a real-time learning algorithm to extend the lifetime of a
Asad M. Madni +3 more
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Data-Driven Markov Decision Process Approximations for Personalized Hypertension Treatment Planning [PDF]
MDM Policy & Practice, 2016 Background: Markov decision process (MDP) models are powerful tools. They enable the derivation of optimal treatment policies but may incur long computational times and generate decision rules that are challenging to interpret by physicians.
Greggory J. Schell PhD +4 more
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Exact finite approximations of average-cost countable Markov Decision Processes [PDF]
arXiv, 2007 For a countable-state Markov decision process we introduce an embedding which produces a finite-state Markov decision process. The finite-state embedded process has the same optimal cost, and moreover, it has the same dynamics as the original process when restricting to the approximating set.
Leizarowitz, Arie, Shwartz, Adam
arxiv +3 more sources
Multi-Vehicle Tracking via Real-Time Detection Probes and a Markov Decision Process Policy [PDF]
Sensors, 2019 Online multi-object tracking (MOT) has broad applications in time-critical video analysis scenarios such as advanced driver-assistance systems (ADASs) and autonomous driving.
Yi Zou +3 more
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Feature Markov Decision Processes [PDF]
Proceedings of the 2nd Conference on Artificial General Intelligence (2009), 2009 General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observations, actions, and rewards. On the other hand, reinforcement learning is well-developed for small finite state Markov Decision Processes (MDPs). So far it is an art performed by human designers to extract the right state representation out of the bare ...
Marcus Hütter
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Bounded-parameter Markov decision processes
Artificial Intelligence, 2000 AbstractIn this paper, we introduce the notion of a bounded-parameter Markov decision process (BMDP) as a generalization of the familiar exact MDP. A bounded-parameter MDP is a set of exact MDPs specified by giving upper and lower bounds on transition probabilities and rewards (all the MDPs in the set share the same state and action space).
Robert Givan +2 more
openalex +3 more sources