Journal ArticleDOI
Continuous action set learning automata for stochastic optimization
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TLDR
The learning model presented here generalizes the traditional model of a learning automaton and requires a lesser number of function evaluations at each step compared to the stochastic approximation.Abstract:
The problem of optimization with noisy measurements is common in many areas of engineering. The only available information is the noise-corrupted value of the objective function at any chosen point in the parameter space. One well-known method for solving this problem is the stochastic approximation procedure. In this paper we consider an adaptive random search procedure, based on the reinforcement-learning paradigm. The learning model presented here generalizes the traditional model of a learning automaton [Narendra and Thathachar, Learning Automata: An Introduction, Prentice Hall, Englewood Cliffs, 1989]. This procedure requires a lesser number of function evaluations at each step compared to the stochastic approximation. The convergence properties of the algorithm are theoretically investigated. Simulation results are presented to show the efficacy of the learning method.read more
Citations
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Journal ArticleDOI
Varieties of learning automata: an overview
M. A. L. Thathachar,P. S. Sastry +1 more
TL;DR: An attempt has been made to bring together the main ideas involved in a unified framework of learning automata and provide pointers to relevant references.
Journal ArticleDOI
Evolutionary dynamics of multi-agent learning: a survey
TL;DR: This article surveys the dynamical models that have been derived for various multi-agent reinforcement learning algorithms, making it possible to study and compare them qualitatively, and provides a roadmap on the progress that has been achieved in analysing the evolutionary dynamics of multi- agent learning.
Journal ArticleDOI
Probabilistic Constrained Load Flow Considering Integration of Wind Power Generation and Electric Vehicles
TL;DR: In this article, a probabilistic constrained load flow (PCLF) problem suitable for modern power systems with wind power generation and electric vehicles (EV) demand or supply is represented.
Journal ArticleDOI
A General CPL-AdS Methodology for Fixing Dynamic Parameters in Dual Environments
De-Shuang Huang,Wen Jiang +1 more
TL;DR: This paper presents a generalized universal decision formula to solve this bottleneck problem of Continuous Point Location with Adaptive d-ary Search and generalized the CPL-AdS strategy with an extending formula, which is capable of tracking an unknown dynamic parameter λ* in both informative and deceptive environments.
Journal ArticleDOI
Mobility prediction in mobile wireless networks
TL;DR: An adaptive learning automata-based mobility prediction method is proposed in which the prediction is made based on the Gauss-Markov random process, and exploiting the correlation of the mobility parameters over time, and results conform to the theoretically expected convergence results.
References
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Journal ArticleDOI
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
TL;DR: This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing stochastic units that are shown to make weight adjustments in a direction that lies along the gradient of expected reinforcement in both immediate-reinforcement tasks and certain limited forms of delayed-reInforcement tasks, and they do this without explicitly computing gradient estimates.
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Multivariate stochastic approximation using a simultaneous perturbation gradient approximation
TL;DR: The paper presents an SA algorithm that is based on a simultaneous perturbation gradient approximation instead of the standard finite-difference approximation of Keifer-Wolfowitz type procedures that can be significantly more efficient than the standard algorithms in large-dimensional problems.
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Minimization by Random Search Techniques
TL;DR: Two general convergence proofs for random search algorithms are given and how these extend those available for specific variants of the conceptual algorithm studied here are shown.
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Multidimensional Stochastic Approximation Methods
TL;DR: In this paper, a multidimensional stochastic approximation scheme is presented, and conditions are given for these schemes to converge a.s.p.s to the solutions of $k-stochastic equations in $k$ unknowns.
Journal ArticleDOI
A stochastic reinforcement learning algorithm for learning real-valued functions
TL;DR: A stochastic reinforcement learning algorithm for learning functions with continuous outputs using a connectionist network that learns to perform an underconstrained positioning task using a simulated 3 degree-of-freedom robot arm.