Reinforcement learning, sequential Monte Carlo and the EM algorithm

DOI10.1007/s12046-018-0889-8zbMath1398.60060OpenAlexW2811208815WikidataQ129645078 ScholiaQ129645078MaRDI QIDQ1615400

Ankush V. Jain, Vivek S. Borkar

Publication date: 31 October 2018

Published in: Sādhanā (Search for Journal in Brave)

Full work available at URL: https://www.ias.ac.in/describe/article/sadh/043/08/0123

zbMATH Keywords

EM algorithmimportance samplingreinforcement learningsequential Monte Carlononlinear filtering and smoothing

Mathematics Subject Classification ID

Markov processes: estimation; hidden Markov models (62M05) Monte Carlo methods (65C05) Learning and adaptive systems in artificial intelligence (68T05) Signal detection and filtering (aspects of stochastic processes) (60G35)

Cites Work

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• A remark on control of partially observed Markov chains
• Fundamentals of stochastic filtering
• Inference in hidden Markov models.
• Stochastic simulation: Algorithms and analysis
• Backward Simulation Methods for Monte Carlo Statistical Inference
• Adaptive Importance Sampling Technique for Markov Chains Using Stochastic Approximation
• On the optimal filtering of diffusion processes

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