We adapt the ideas underlying the success of Deep Q-Learning to the continuous action
domain. We present an actor-critic, model-free algorithm based on the deterministic policy …

With the ubiquitous deployment of a massive number of Internet-of-Things (IoT) devices, the
satellite-aerial networks are becoming a promising candidate to provide flexible and …

The use of deep reinforcement learning allows for high-dimensional state descriptors, but
little is known about how the choice of action representation impacts learning and the …

This study proposes a method based on Bayesian networks (BNs) to optimize the reliability
of gas supply in natural gas pipeline networks. The method integrates probabilistic safety …

The physical design of a robot and the policy that controls its motion are inherently coupled,
and should be determined according to the task and environment. In an increasing number …

The autonomous landing of an unmanned aerial vehicle (UAV) is still an open problem.
Previous work focused on the use of hand-crafted geometric features and sensor-data fusion …

Spectrum scarcity and energy limitation are becoming two critical issues in designing
Internet of Things (IoT). As two promising technologies, cognitive radio (CR) and radio …

Deep reinforcement learning has received considerable attention after the outstanding
performance of AlphaGo. In this paper, we propose a new control strategy of self-driving …

In this paper, we present a hybrid ambient backscatter communication (ABC) assisted
framework for radio frequency (RF) powered cognitive radio networks (CRNs). In these …

Reinforcement Learning (RL) based control algorithms can learn the control strategies for
nonlinear and uncertain environment during interacting with it. Guided by the rewards …