Policy space identification in configurable environments

DOI10.1007/s10994-021-06033-3OpenAlexW3196519516MaRDI QIDQ2163245

Guglielmo Manneschi, Alberto Maria Metelli, Marcello Restelli

Publication date: 10 August 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1909.03984

zbMATH Keywords

likelihood ratio testreinforcement learningconfigurable Markov decision processespolicy space identification

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• SBEED
• PILCO
• SQIL

Cites Work

• The philosophy of Bayes factors and the quantification of statistical evidence
• A tail inequality for quadratic forms of subgaussian random vectors
• Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path
• Rates of convergence for empirical processes of stationary mixing sequences
• Generalized inverses. Theory and applications.
• Concentration Inequalities
• Nonasymptotic sequential tests for overlapping hypotheses applied to near-optimal arm identification in bandit models
• Identification in Parametric Models
• The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses
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