Results 271 to 280 of about 198,528 (317)

Information-Theoretic Intrinsic Motivation for Reinforcement Learning in Combinatorial Routing. [PDF]

Entropy (Basel)
Xi R, Ni Y, Wu W.
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SyntheMol-RL: a flexible reinforcement learning framework for designing easily synthesizable antibiotics. [PDF]

Mol Syst Biol
Swanson K, Liu G, Catacutan DB, McLellan S, Arnold A, Tu MM, Brown ED, Zou J, Stokes JM.   +8 more
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Portfolio management based on value distribution reinforcement learning algorithm. [PDF]

Front Artif Intell
Yang Y, Wang T, Fu Y, Huang J, Zhou D.
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Correction to: Deep reinforcement learning for automatic anatomic CT landmark localization in Stanford Type B aortic dissection. [PDF]

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

Dao DN, Miao Y.
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Reference Point-Dependent Reinforcement Learning in Humans and Rats

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

Robust Reinforcement Learning

Neural Computation, 2005
This letter proposes a new reinforcement learning (RL) paradigm that explicitly takes into account input disturbance as well as modeling errors. The use of environmental models in RL is quite popular for both off-line learning using simulations and for online action planning.
Jun Morimoto, Kenji Doya
openaire   +3 more sources