bit-mcs Goto Github PK

attconvlstm

[ICDE 2021] Modeling Citywide Crowd Flows using Attentive Convolutional LSTM

cadre

[AAAI 2022] CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Driving

drl-cews

[ICDE 2020] Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning Approach

drl-disastervc

[KDD 2021] Energy-Efficient 3D Vehicular Crowdsourcing for Disaster Response by Distributed Deep Reinforcement Learning

drl-ec3

[JSAC 2018] Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach

drl-efresh

[TMC 2023] Delay-Sensitive Energy-Efficient UAV Crowdsensing by Deep Reinforcement Learning

drl-freshmcs

[INFOCOM 2021] Mobile Crowdsensing for Data Freshness: A Deep Reinforcement Learning Approach

drl-mtvcs

[INFOCOM 2020] Multi-Task-Oriented Vehicular Crowdsensing: A Deep Learning Approach

drl-sim

[TITS 2021] Social-aware incentive mechanism for vehicular crowdsensing by deep reinforcement learning

drl-ucs-aoi-threshold

[ToN 2024] Ensuring Threshold AoI for UAV-assisted Mobile Crowdsensing by Multi-Agent Deep Reinforcement Learning with Transformer

e-divert

[TMC 2021] Distributed and Energy-Efficient Mobile Crowdsensing with Charging Stations by Deep Reinforcement Learning

edics

[JSAC 2019] Energy-Efficient Distributed Mobile Crowd Sensing: A Deep Learning Approach

gcrl-min-aoi

[INFOCOM 2022] AoI-minimal UAV Crowdsensing by Model-based Graph Convolutional Reinforcement Learning

hi-madrl

[ICDE 2023] Exploring both Individuality and Cooperation for Air-Ground Spatial Crowdsourcing by Multi-Agent Deep Reinforcement Learning

human_drone_sc

[ICDE 2022] Human-Drone Collaborative Spatial Crowdsourcing by Memory-Augmented Distributed Multi-Agent Deep Reinforcement Learning

jppo-convntm

[INFOCOM 2020] Energy-Efficient UAV Crowdsensing with Multiple Charging Stations by Deep Learning

madrl-posvcs

sims

[TNSE 2021] Modeling User Interests With Online Social Network Influence by Memory Augmented Sequence Learning

time-aware-location-prediction

[TKDE 2022] Time-Aware Location Prediction by Convolutional Area-of-Interest Modeling and Memory-Augmented Attentive LSTM