Results 221 to 230 of about 112,162 (267)

Adaptive training load optimization for track and field athletes: A reinforcement learning approach. [PDF]

Sci Rep
Zhang Q, Wang Q, Niu Y.
europepmc   +1 more source

QRBT: Quantum Driven Reinforcement Learning for Scalable Blockchain Transaction Processing. [PDF]

PLoS One
Lella KK, Mallu SRK.
europepmc   +1 more source

Retraction: Towards robot-assisted therapy for children with autism-the ontological knowledge models and reinforcement learning-based algorithms. [PDF]

Front Robot AI
Frontiers Editorial Office.
europepmc   +1 more source

Categorization and Reinforcement Learning: State Identification in Reinforcement Learning and Network Reinforcement Learning

, 2007
Veksler, Vladislav D., Gray, Wayne D., Schoelles, Micheal J.   +2 more
openaire   +1 more source

BeamCraft: Deep Reinforcement Learning-DrivenMulti-Objective Beamforming for ISAC

Dao DN, Miao Y.
europepmc   +1 more source

Correction to: Deep reinforcement learning for automatic anatomic CT landmark localization in Stanford Type B aortic dissection. [PDF]

Radiol Adv
europepmc   +1 more source

Reference Point-Dependent Reinforcement Learning in Humans and Rats

Palminteri S, Ferguson L, Soukupova M, Bouret S, Parkes S.   +4 more
europepmc   +1 more source

Dopamine drives a positive reward bias on human reinforcement learning

Zalta A, Skvortsova V, Hewitt SR, Moutoussis M, Nour MM, Dolan RJ, Findling C, Hauser TU, Wyart V.   +8 more
europepmc   +1 more source

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Learning Pessimism for Reinforcement Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 2023
Off-policy deep reinforcement learning algorithms commonly compensate for overestimation bias during temporal-difference learning by utilizing pessimistic estimates of the expected target returns. In this work, we propose Generalized Pessimism Learning (GPL), a strategy employing a novel learnable penalty to enact such pessimism.
Edoardo Cetin, Oya Çeliktutan
openaire   +1 more source

Meta-learning in Reinforcement Learning

Neural Networks, 2003
Meta-parameters in reinforcement learning should be tuned to the environmental dynamics and the animal performance. Here, we propose a biologically plausible meta-reinforcement learning algorithm for tuning these meta-parameters in a dynamic, adaptive manner.
Nicolas Schweighofer, Kenji Doya
openaire   +2 more sources