Actively learning costly reward functions for reinforcement learning

Transfer of recent advances in deep reinforcement learning to real-world applications is hindered by high data demands and thus low efficiency and scalability. Through independent improvements of components such as replay buffers or more stable learning algorithms, and through massively distributed...

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Veröffentlicht in:	Machine learning: science and technology 2024-03, Vol.5 (1), p.15055
Hauptverfasser:	Eberhard, André, Metni, Houssam, Fahland, Georg, Stroh, Alexander, Friederich, Pascal
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Buffers (chemistry) Computer networks Computer simulation Deep learning Design optimization Machine learning natural science Neural networks Optimization reinforcement learning
Online-Zugang:	Volltext
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