5 citations · 9 across the 6 of their papers we have counts for
6 papers
Simulation-Based Optimistic Policy Iteration For Multi-Agent MDPs with Kullback-Leibler Control Cost
Khaled Nakhleh, Ceyhun Eksin, Sabit Ekin
This paper proposes an agent-based optimistic policy iteration (OPI) scheme for learning stationary optimal stochastic policies in multi-agent Markov Decision Processes (MDPs), in…
AoI, Timely-Throughput, and Beyond: A Theory of Second-Order Wireless Network Optimization
Daojing Guo, Khaled Nakhleh, I-Hong Hou +2
This paper introduces a new theoretical framework for optimizing second-order behaviors of wireless networks. Unlike existing techniques for network utility maximization, which onl…
SACPlanner: Real-World Collision Avoidance with a Soft Actor Critic Local Planner and Polar State Representations
Khaled Nakhleh, Minahil Raza, Mack Tang +6
We study the training performance of ROS local planners based on Reinforcement Learning (RL), and the trajectories they produce on real-world robots. We show that recent enhancemen…
DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs
Khaled Nakhleh, I-Hong Hou
We consider the problem of learning the optimal threshold policy for control problems. Threshold policies make control decisions by evaluating whether an element of the system stat…
A Theory of Second-Order Wireless Network Optimization and Its Application on AoI
Daojing Guo, Khaled Nakhleh, I-Hong Hou +2
This paper introduces a new theoretical framework for optimizing second-order behaviors of wireless networks. Unlike existing techniques for network utility maximization, which onl…
NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL
Khaled Nakhleh, Santosh Ganji, Ping-Chun Hsieh +2
Whittle index policy is a powerful tool to obtain asymptotically optimal solutions for the notoriously intractable problem of restless bandits. However, finding the Whittle indices…