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Found 4 papers, 3 papers with code
An Efficient Generalizable Framework for Visuomotor Policies via Control-aware Augmentation and Privilege-guided Distillation
Visuomotor policies, which learn control mechanisms directly from high-dimensional visual observations, confront challenges in adapting to new environments with intricate visual variations.
CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Driving
Vision-based autonomous urban driving in dense traffic is quite challenging due to the complicated urban environment and the dynamics of the driving behaviors.
Social-Aware Incentive Mechanism for VehicularCrowdsensing by Deep Reinforcement Learning
Vehicular crowdsensing (VCS) takes the advantage of vehicles’ mobility and exploits both the crowd wisdom and sensing abilities offered by vehicle drivers’ carried smart mobile devices and on-board sensors to accomplish challenging sensing tasks.
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning Approach
Spatial crowdsourcing (SC) utilizes the potential of a crowd to accomplish certain location based tasks.
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