Accelerated structure-aware reinforcement learning for delay-sensitive energy harvesting wireless sensors

Keyphrases

• Reinforcement Learning Algorithm 100%
• Delay-sensitive 100%
• Reinforcement Learning 100%
• Energy Harvesting Wireless Sensor Network (EH-WSN) 100%
• Structure-aware 100%
• Memory Complexity 66%
• Increasing Differences 66%
• Buffer State 66%
• Battery State 66%
• Computational Complexity 33%
• Approximation Error 33%
• Value Function 33%
• Computational Memory 33%
• Order of Magnitude 33%
• Performance Gain 33%
• Scheduling Problem 33%
• Popular 33%
• Scheduling Policy 33%
• Energy Harvesting 33%
• Stochastic Processes 33%
• Online Learning 33%
• State Space 33%
• Optimal Scheduling 33%
• Fading Channels 33%
• Queuing Delay 33%
• System State 33%
• Channel State 33%
• Nonincreasing 33%
• Nondecreasing 33%
• State Dynamics 33%
• Full Knowledge 33%
• Data Packet 33%
• Competitive Performance 33%
• Buffer Battery 33%
• Markov Decision Process 33%
• Structured Knowledge 33%
• Value Function Approximation 33%
• Approximation Accuracy 33%
• Q-learning 33%
• Ambient Energy 33%
• Energy Dynamics 33%
• Optimal Value Function 33%
• Accelerated Reinforcement Learning 33%
• Learning to Optimize 33%
• Delay Sensitive Data 33%
• Function Structure 33%
• Learning Reinforcement 33%

Computer Science

• Wireless Sensor 100%
• Reinforcement Learning 100%
• Approximation (Algorithm) 50%
• Function Value 50%
• And-States 50%
• Scheduling Policy 50%
• Structure Function 25%
• Performance Gain 25%
• Scheduling Problem 25%
• Fading Channel 25%
• Online Learning 25%
• State Space 25%
• Learning Technique 25%
• Relative Performance 25%
• Function Approximation 25%
• Knowledge Structure 25%
• Markov Decision Process 25%