Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications
Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications explores A delay-tolerant multi-agent deep reinforcement learning algorithm for UAV-assisted wireless networks to improve throughput and reduce information delay.. Commercial viability score: 3/10 in UAV-Assisted Wireless Networks.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Prior WorkCommunication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV DeploymentViability: 3.0
- Higher ViabilityUAV-MARL: Multi-Agent Reinforcement Learning for Time-Critical and Dynamic Medical Supply DeliveryViability: 7.0
- Higher ViabilityAgentic AI for Embodied-enhanced Beam Prediction in Low-Altitude Economy NetworksViability: 7.0
- Higher ViabilityRobust Distributed Cooperative Path-Following and Local Replanning for Multi-UAVs Under Differentiated Low-Altitude PathsViability: 5.0
- Higher Viability