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Tackling Uncertainty in Reinforcement Learning: A Dual Variational Inference Approach for Task and State Estimation

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Bibliographic Details
Published in:International Journal of Advanced Network, Monitoring and Controls
Format: Online Article RSS Article
Published: 2025
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container_title International Journal of Advanced Network, Monitoring and Controls
description
discipline_display Technology & Engineering
discipline_facet Technology & Engineering
format Online Article
RSS Article
genre Journal Article
id rss_article:12500
institution FRELIP
journal_source_facet International Journal of Advanced Network, Monitoring and Controls
publishDate 2025
publishDateSort 2025
record_format rss_article
spellingShingle Tackling Uncertainty in Reinforcement Learning: A Dual Variational Inference Approach for Task and State Estimation
Software
Technology & Engineering — Computing
Technology & Engineering
sub_discipline_display Technology & Engineering — Computing
sub_discipline_facet Technology & Engineering — Computing
subject_display Software
Technology & Engineering — Computing
Technology & Engineering
Software
Technology & Engineering — Computing
Technology & Engineering
subject_facet Software
Technology & Engineering — Computing
Technology & Engineering
title Tackling Uncertainty in Reinforcement Learning: A Dual Variational Inference Approach for Task and State Estimation
title_auth Tackling Uncertainty in Reinforcement Learning: A Dual Variational Inference Approach for Task and State Estimation
title_full Tackling Uncertainty in Reinforcement Learning: A Dual Variational Inference Approach for Task and State Estimation
title_fullStr Tackling Uncertainty in Reinforcement Learning: A Dual Variational Inference Approach for Task and State Estimation
title_full_unstemmed Tackling Uncertainty in Reinforcement Learning: A Dual Variational Inference Approach for Task and State Estimation
title_short Tackling Uncertainty in Reinforcement Learning: A Dual Variational Inference Approach for Task and State Estimation
title_sort tackling uncertainty in reinforcement learning: a dual variational inference approach for task and state estimation
topic Software
Technology & Engineering — Computing
Technology & Engineering
url https://sciendo.com/article/10.2478/ijanmc-2025-0030