Results 101 to 110 of about 88,537 (171)

Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units. [PDF]

BMC Med Inform Decis Mak, 2019
Yu C, Liu J, Zhao H.
europepmc   +1 more source

Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning. [PDF]

Int J Comput Assist Radiol Surg
Robertshaw H, Karstensen L, Jackson B, Granados A, Booth TC.   +4 more
europepmc   +1 more source

A Semiparametric Inverse Reinforcement Learning Approach to Characterize Decision Making for Mental Disorders. [PDF]

J Am Stat Assoc
Guo X, Zeng D, Wang Y.
europepmc   +1 more source

Modular inverse reinforcement learning for visuomotor behavior. [PDF]

Biol Cybern, 2013
Rothkopf CA, Ballard DH.
europepmc   +1 more source

Score-based Inverse Reinforcement Learning

, 2016
This paper reports theoretical and empirical results obtained for the score-based Inverse Reinforcement Learning (IRL) algorithm. It relies on a non-standard setting for IRL consisting of learning a reward from a set of globally scored trajec-tories. This allows using any type of policy (optimal or not) to generate trajectories without prior knowledge ...
El Asri, Layla, Piot, Bilal, Geist, Matthieu, Laroche, Romain, Pietquin, Olivier   +4 more
openaire   +1 more source

Towards Generalized Inverse Reinforcement Learning

This paper studies generalized inverse reinforcement learning (GIRL) in Markov decision processes (MDPs), that is, the problem of learning the basic components of an MDP given observed behavior (policy) that might not be optimal. These components include not only the reward function and transition probability matrices, but also the action space and ...
Dong, Chaosheng, Wang, Yijia
openaire   +1 more source

Reinforcement learning-driven deep learning approaches for optimized robot trajectory planning. [PDF]

Sci Rep
Shiyu F.
europepmc   +1 more source

Consensus Paper: Models of Cerebellar Functions. [PDF]

Cerebellum
Kakei S, Bostan AC, Ebner TJ, Fakharian MA, Gomi H, Guell X, Hemelt M, Hoang H, Hull C, Inoue M, Ishikawa T, Kameda M, Kawato M, Kitazawa S, Manto M, Medina JF, Mitoma H, Ohmae K, Ohmae S, Okada KI, Popa LS, Schmahmann JD, Shadmehr R, Strick PL, Tanaka H, Tanaka M, Yamazaki T.   +26 more
europepmc   +1 more source

Virtualization as a New Scaling Law for Semiconductor Devices Beyond Geometric Scaling. [PDF]

Small
Wang Z, Chen X, Lin F, Li X, Wang F, Guo S, Yu S, Li L, Huang JK.   +8 more
europepmc   +1 more source

Participatory-informed preference optimization (PiPrO): A reinforcement learning simulation study. [PDF]

PLOS Digit Health
Templin T, Song S, Fort S, Sinnott-Armstrong N.   +3 more
europepmc   +1 more source