Multi-Agent Deep Reinforcement Learning for Spectrum Management in V2X with Social Roles

Keyphrases

• System Utility 100%
• Spectrum Management 100%
• V2X Communication 100%
• Social Roles 100%
• Multi-agent Deep Reinforcement Learning (MADRL) 100%
• Role-oriented 100%
• Decision Basis 50%
• Heavy Traffic 50%
• Subchannel 50%
• Power Level 50%
• Transmission Mode 50%
• Vehicle Type 50%
• Multiple Vehicle Types 50%
• Actor-critic 50%
• Effective Spectrum 50%
• Agent Types 50%
• Actor-critic Model 50%
• Role Expansion 50%
• Communication Policy 50%
• Vehicle-to-everything Communication 50%
• Normalized Systems 50%

Engineering

• Reinforcement Learning 100%
• Spectrum Management 100%
• Communication System 50%
• Power Level 50%
• Practical Problem 50%
• Adaptability 50%
• Communication Policy 50%

Computer Science

• Utility Software 100%
• Spectrum Management 100%
• Multi-Agent Deep Reinforcement Learning 100%
• Communication Policy 50%