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

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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 Software
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format Online Article
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genre Journal Article
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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
General
Software
sub_discipline_display General
sub_discipline_facet General
subject_display Software
General
Software
Software
General
Software
subject_facet Software
General
Software
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
General
Software
url https://sciendo.com/article/10.2478/ijanmc-2025-0030