Demand-side scheduling based on multi-agent deep actor-critic learning for smart grids

Keyphrases

• Multi-agent 100%
• Partial Observability 100%
• Smart Grid 100%
• Demand Side 100%
• Price Signals 100%
• Actor-critic Learning 100%
• Decentralized Agents 100%
• Centralized Approach 50%
• Overall Cost 50%
• Non-stationarity 50%
• Reinforcement Learning Algorithm 50%
• Privacy-preserving 50%
• Realistic Settings 50%
• Scheduling Algorithm 50%
• Energy Demand 50%
• Home Appliances 50%
• Complete Observability 50%
• Centralized Learning 50%
• Centralized Critic 50%
• Electricity Cost 50%
• Smart Grid Environment 50%
• Actor-critic Algorithm 50%
• Markov Games 50%
• Household-based 50%
• Total Energy Consumed 50%
• Demand-side Energy Management 50%
• Smart Meter 50%
• Real-time Pricing 50%
• Online Deep Reinforcement Learning 50%
• Decentralized Execution 50%
• Grid Operator 50%
• Peak-to-average Ratio 50%

Computer Science

• multi agent 100%
• Partial Observability 100%
• Smart Grid 100%
• Scheduling Algorithm 50%
• Privacy Preservation 50%
• Deep Reinforcement Learning 50%
• Smart Meters 50%
• Smart Grid Environment 50%
• Centralized Approach 50%
• Home Appliance 50%

Chemical Engineering

• Scalability 100%
• Reinforcement Learning 100%