Multi-Agent Deep Reinforcement Learning for Energy-Efficient UAV-Aided Hybrid NOMA Data Collection

Computer Science

• Energy Efficient 100%
• Multi-Agent Deep Reinforcement Learning 100%
• Unmanned Aerial Vehicle 100%
• Non-Orthogonal Multiple Access 100%
• Optimization Problem 40%
• Speed-up 20%
• Power Allocation 20%
• Collection System 20%
• multi agent 20%
• Energy Efficiency 20%
• Learning Approach 20%
• Continuous Variable 20%
• Overestimation 20%

Engineering

• Unmanned Aerial Vehicle 100%
• Non-Orthogonal Multiple Access 100%
• Multi-Agent Deep Reinforcement Learning 100%
• Optimisation Problem 40%
• Learning Approach 20%
• Power Allocation 20%
• Q-Learning 20%
• Convex Problem 20%
• Continuous Variable 20%
• Joints (Structural Components) 20%
• Conservation of Energy 20%

Chemical Engineering

• Deep Reinforcement Learning 100%