0957-4174/$ see front matter 2013 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2013.01.035 ⇑ Corresponding author. Tel.: +1 514 577 9759. E-mail addresses: [email protected] (A.H.R. K (R. Sabourin), [email protected] (F. Gagnon). This paper introduces a novel multi-agent multi-state reinforcement learning exploration scheme for dynamic spectrum access and dynamic spectrum sharing ...

Reinforcement learning, augmented by the representational power of deep neural networks, has shown promising results on high-dimensional problems, such as game playing and robotic control. However, sequential nature these problems poses a fundamental challenge for computational efficiency. Recently, alternative approaches evolutionary strategies neuroevolution demonstrated competitive with fast...

Packet routing is one of the fundamental problems in computer networks which a router determines next-hop each packet queue to get it as quickly possible its destination. Reinforcement learning (RL) has been introduced design autonomous policies with local information stochastic arrival and service. However, curse dimensionality RL prohibits more comprehensive representation dynamic network sta...

Over the past few years, use of swarms Unmanned Aerial Vehicles (UAVs) in monitoring and remote area surveillance applications has become widespread thanks to price reduction increased capabilities drones. The drones swarm need cooperatively explore an unknown area, order identify monitor interesting targets, while minimizing their movements. In this work, we propose a distributed Reinforcement...

Temporal-difference (TD) algorithms have been proposed as models of reinforcement learning (RL). We examine two issues of distributed representation in these TD algorithms: distributed representations of belief and distributed discounting factors. Distributed representation of belief allows the believed state of the world to distribute across sets of equivalent states. Distributed exponential d...

Reinforcement learning is a general and powerful way to formulate complex learning problems and acquire good system behaviour. The goal of a reinforcement learning system is to maximize a long term sum of instantaneous rewards provided by a teacher. In its extremum form, reinforcement learning only requires that the teacher can provide a measure of success. This formulation does not require a t...