15 citations · 32 across the 7 of their papers we have counts for
6 papers
Dynamic Noises of Multi-Agent Environments Can Improve Generalization: Agent-based Models meets Reinforcement Learning
Mohamed Akrout, Amal Feriani, Bob McLeod
We study the benefits of reinforcement learning (RL) environments based on agent-based models (ABM). While ABMs are known to offer microfoundational simulations at the cost of comp…
On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems
Amal Feriani, Amine Mezghani, Ekram Hossain
We consider an Intelligent Reflecting Surface (IRS)-aided multiple-input single-output (MISO) system for downlink transmission. We compare the performance of Deep Reinforcement Lea…
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty
Yuanchao Xu, Amal Feriani, Ekram Hossain
Multi-Agent Reinforcement Learning (MARL) is a challenging subarea of Reinforcement Learning due to the non-stationarity of the environments and the large dimensionality of the com…
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial
Amal Feriani, Ekram Hossain
Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have led to multiple successes in solving sequential decision-making problems in various domains,…
Hacking Google reCAPTCHA v3 using Reinforcement Learning
Ismail Akrout, Amal Feriani, Mohamed Akrout
We present a Reinforcement Learning (RL) methodology to bypass Google reCAPTCHA v3. We formulate the problem as a grid world where the agent learns how to move the mouse and click…
DVOLVER: Efficient Pareto-Optimal Neural Network Architecture Search
Guillaume Michel, Mohammed Amine Alaoui, Alice Lebois +2
Automatic search of neural network architectures is a standing research topic. In addition to the fact that it presents a faster alternative to hand-designed architectures, it can…