Results 41 to 50 of about 6,930,474 (290)
Constrained Reinforcement Learning for Short Video Recommendation
CoRR, 2022 The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users provide complex and multi-faceted responses towards recommendations, including watch time and various types of interactions with videos.
Qingpeng Cai 0001 +7 more
openaire +3 more sources
Curriculum reinforcement learning via constrained optimal transport
, 2022 Curriculum reinforcement learning (CRL) allows solving complex tasks by generating a tailored sequence of learning tasks, starting from easy ones and subsequently increasing their difficulty.
Klink, Pascal +4 more
core +1 more source
Resource Constrained Deep Reinforcement Learning
Proceedings of the International Conference on Automated Planning and Scheduling, 2019 In urban environments, resources have to be constantly matched to the “right” locations where customer demand is present. For instance, ambulances have to be matched to base stations regularly so as to reduce response time for emergency incidents in ERS (Emergency Response Systems); vehicles (cars, bikes among others) have to be matched to docking ...
BHATIA, Abhinav +2 more
openaire +3 more sources
Continuous Doubly Constrained Batch Reinforcement Learning
CoRR, 2021 NeurIPS 2021 conference ...
Rasool Fakoor +4 more
openaire +3 more sources
Safety-Constrained Reinforcement Learning for MDPs [PDF]
, 2016 We consider controller synthesis for stochastic and partially unknown environments in which safety is essential. Specifically, we abstract the problem as a Markov decision process in which the expected performance is measured using a cost function that is unknown prior to run-time exploration of the state space.
Sebastian Junges +4 more
openaire +3 more sources
Identifiability and Generalizability in Constrained Inverse Reinforcement Learning
CoRR, 2023 Two main challenges in Reinforcement Learning (RL) are designing appropriate reward functions and ensuring the safety of the learned policy. To address these challenges, we present a theoretical framework for Inverse Reinforcement Learning (IRL) in constrained Markov decision processes.
Andreas Schlaginhaufen +1 more
openaire +3 more sources
Robotics, 2022 Rule-based traditional motion planning methods usually perform well with prior knowledge of the macro-scale environments but encounter challenges in unknown and uncertain environments.
Shangding Gu +5 more
doaj +1 more source
Online Safe Flight Control Method Based on Constraint Reinforcement Learning
DronesUAVs are increasingly prominent in the competition for space due to their multiple characteristics, such as strong maneuverability, long flight distance, and high survivability.
Jiawei Zhao +3 more
doaj +1 more source
Online Constrained Model-based Reinforcement Learning
CoRR, 2020 Conf.
Benjamin van Niekerk +2 more
openaire +4 more sources
Memory and Resting‐State Connectivity in Acute Transient Global Amnesia: A Case–Control fMRI Study
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background and Objectives Transient global amnesia (TGA) is a striking model of isolated amnesia. While hippocampal lesions are well described, the network‐level mechanisms and the precise neuropsychological profile remain debated. Our objective was thus to characterize functional and neuropsychological correlates of acute TGA and their ...
Elias El Otmani +10 more
wiley +1 more source