Learning and planning in partially observable environments without prior domain knowledge

DOI10.1016/j.ijar.2021.12.004OpenAlexW4200234845MaRDI QIDQ2076979

Yunlong Liu, Jianyang Zheng, Fangfang Chang

Publication date: 22 February 2022

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2021.12.004

zbMATH Keywords

Monte-Carlo tree searchpredictive state representationsplan under partial observabilityprior domain knowledge

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Uses Software

• POMDPS
• DESPOT

Cites Work

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• Planning and acting in partially observable stochastic domains
• Spectral learning of weighted automata. A forward-backward perspective
• A sparse sampling algorithm for near-optimal planning in large Markov decision processes
• Convex Optimization: Algorithms and Complexity
• Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search
• DESPOT: Online POMDP Planning with Regularization
• Robust Control of Markov Decision Processes with Uncertain Transition Matrices

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