7 citations · 7 across the 5 of their papers we have counts for
5 papers
Policy Optimization in Multi-Agent Settings under Partially Observable Environments
Ainur Zhaikhan, Malek Khammassi, Ali H. Sayed
This work leverages adaptive social learning to estimate partially observable global states in multi-agent reinforcement learning (MARL) problems. Unlike existing methods, the prop…
Energy-as-a-Service for RF-Powered IoE Networks: A Percolation Theory Approach
Hao Lin, Ainur Zhaikhan, Mustafa A. Kishk +2
Due to the involved massive number of devices, radio frequency (RF) energy harvesting is indispensable to realize the foreseen Internet-of-Everything (IoE) within 6G networks. Anal…
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments
Ainur Zhaikhan, Ali H. Sayed
This study proposes the use of a social learning method to estimate a global state within a multi-agent off-policy actor-critic algorithm for reinforcement learning (RL) operating…
Graph Exploration for Effective Multi-agent Q-Learning
Ainur Zhaikhan, Ali H. Sayed
This paper proposes an exploration technique for multi-agent reinforcement learning (MARL) with graph-based communication among agents. We assume the individual rewards received by…
Safeguarding the IoT from Malware Epidemics: A Percolation Theory Approach
Ainur Zhaikhan, Mustafa A. Kishk, Hesham ElSawy +1
The upcoming Internet of things (IoT) is foreseen to encompass massive numbers of connected devices, smart objects, and cyber-physical systems. Due to the large-scale and massive d…