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7 days ago · Our objective is to maximize the system throughput while ensuring fairness by optimizing decisions such as user-UAV association, subchannel number, subchannel ...
Missing: Making | Show results with:Making
4 days ago · In this paper, we propose the use of multi-hop data offloading using Multi-Agent Reinforcement Learning (MARL) to decide the fraction of data that an agent will ...
5 days ago · In multi-agent search operations, a common strategy is for each UAV to move towards high-probability locations while maintaining a safe distance from other UAVs ...
3 days ago · DRL combines reinforcement learning principles with deep learning techniques, allowing UAVs to learn optimal navigation strategies through trial-and-error ...
5 days ago · The platooning problem is modeled as a multi-agent cooperative decision-making challenge based on Trust Region Policy Optimization (TRPO) [23] , with a twin- ...
6 days ago · The core principle of DWA is to evaluate potential velocities to determine the most suitable trajectory for the UAV in the short term. Jorge Bes et al. [43] ...
3 days ago · deep learning framework named IOPO. IOPO is designed to jointly optimize offloading decisions of the multi-user multi-uav system and the phase shift of the IRS.
Missing: Collaborative | Show results with:Collaborative
6 days ago · In this paper, we perform a broad and thorough investigation on training acceleration methodologies for deep reinforcement learning based on parallel and ...
Missing: Collaborative | Show results with:Collaborative
6 days ago · The method can select robot skills from end-to-end policies based on reinforcement learning and classic map-based planning methods. The proposed approach ...
1 day ago · Collaborative Attack Sequence Generation Model Based on Multiagent Reinforcement Learning for Intelligent Traffic Signal System ... Intelligent Decision-Making ...