We investigate flow rate optimization on a wireless link with randomly varying channel gain using techniques from adaptive modulation and network utility maximization. We consider ...
This paper investigates a novel model-free reinforcement learning architecture, the Natural Actor-Critic. The actor updates are based on stochastic policy gradients employing Amari...
This paper addresses the problem of extending the lifetime of a batterypowered mobile host in a client-server wireless network by using task migration and remote processing. This ...
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
With the goal to generate more scalable algorithms with higher efficiency and fewer open parameters, reinforcement learning (RL) has recently moved towards combining classical tec...