Multi-robot inverse reinforcement learning under occlusion with estimation of state transitions

DOI10.1016/j.artint.2018.07.002zbMath1473.68192OpenAlexW2857457978WikidataQ129544634 ScholiaQ129544634MaRDI QIDQ1731836

Kenneth Bogert, Prashant Doshi

Publication date: 14 March 2019

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2018.07.002

zbMATH Keywords

maximum entropyroboticsmachine learninginverse reinforcement learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Artificial intelligence for robotics (68T40)

Related Items (1)

Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems

Uses Software

• LBFGS-B

Cites Work

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• The EM algorithm for graphical association models with missing data
• Estimation of State Probabilities Using the Maximum Entropy Principle
• A Limited Memory Algorithm for Bound Constrained Optimization
• A Simplex Method for Function Minimization
• The Complexity of Decentralized Control of Markov Decision Processes

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