Deep variational reinforcement learning for POMDPs

Deep variational reinforcement learning for POMDPs

Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of incomplete and noisy observations. In this...

Ausführliche Beschreibung

Bibliographische Detailangaben
Hauptverfasser: Igl, M, Zintgraf, L, Le, T, Wood, F, Whiteson, S
Format: Conference item
Veröffentlicht: Journal of Machine Learning Research 2018

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