Results 51 to 60 of about 7,880,220 (196)
A tutorial introduction to reinforcement learning
SICE Journal of Control, Measurement, and System Integration, 2023 In this paper, we present a brief survey of reinforcement learning, with particular emphasis on stochastic approximation (SA) as a unifying theme. The scope of the paper includes Markov reward processes, Markov decision processes, SA algorithms, and ...
Mathukumalli Vidyasagar
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Safe Q-Learning Method Based on Constrained Markov Decision Processes
IEEE Access, 2019 The application of reinforcement learning in industrial fields makes the safety problem of the agent a research hotspot. Traditional methods mainly alter the objective function and the exploration process of the agent to address the safety problem. Those
Yangyang Ge +3 more
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On the detection of Markov decision processes
AutomaticaWe study the detection problem for a finite set of Markov decision processes (MDPs) where the MDPs have the same state and action spaces but possibly different probabilistic transition functions. Any one of these MDPs could be the model for some underlying controlled stochastic process, but it is unknown a priori which MDP is the ground truth.
Xiaoming Duan +4 more
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Dynamic Watermarking for Finite Markov Decision Processes
IEEE Open Journal of Control SystemsDynamic watermarking is an active intrusion detection technique that can potentially detect replay attacks, spoofing attacks, and deception attacks in the feedback channel for control systems.
Jiacheng Tang +2 more
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Life is Random, Time is Not: Markov Decision Processes with Window Objectives [PDF]
Logical Methods in Computer Science, 2020 The window mechanism was introduced by Chatterjee et al. to strengthen classical game objectives with time bounds. It permits to synthesize system controllers that exhibit acceptable behaviors within a configurable time frame, all along their infinite ...
Thomas Brihaye +3 more
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First passage risk probability optimality for continuous time Markov decision processes [PDF]
, 2019 summary:In this paper, we study continuous time Markov decision processes (CTMDPs) with a denumerable state space, a Borel action space, unbounded transition rates and nonnegative reward function.
Wen, Xian, Huo, Haifeng
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Characterizing Markov Decision Processes [PDF]
, 2002 Problem characteristics often have a significant influence on the difficulty of solving optimization problems. In this paper, we propose attributes for characterizing Markov Decision Processes (MDPs), and discuss how they affect the performance of reinforcement learning algorithms that use function approximation.
Bohdana Ratitch, Doina Precup
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Contextual Markov Decision Processes
CoRR, 2015 We consider a planning problem where the dynamics and rewards of the environment depend on a hidden static parameter referred to as the context. The objective is to learn a strategy that maximizes the accumulated reward across all contexts. The new model, called Contextual Markov Decision Process (CMDP), can model a customer's behavior when interacting
Assaf Hallak +2 more
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Bounded parameter Markov decision processes with average reward criterion
, 2007 . Bounded parameter Markov Decision Processes (BMDPs) address the issue of dealing with uncertainty in the parameters of a Markov Decision Process (MDP).
Tewari, Ambuj +3 more
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The Lévy–Khintchine type operators with variable Lipschitz continuous coefficients generate linear or nonlinear Markov processes and semigroups [PDF]
, 2011 Ito's construction of Markovian solutions to stochastic equations driven by a Lévy noise is extended to nonlinear distribution dependent integrands aiming at the effective construction of linear and nonlinear Markov semigroups and the corresponding ...
Kolokoltsov, V. N. (Vasiliĭ Nikitich)
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