… RL has been effectively applied in many important areas of real life. This article intends to
provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. …

… learn from feedback via unimodal or multimodal sensory input. This paper reviews methods
for interactive reinforcement learning agent to learn … human-human interaction in the real life. …

… Despite all of these successful applications for UAVs in real life, their benefits on the
commercial level and its autonomous mode of operation were not sufficient to allow free-provided …

… in the areas of transfer learning and domain adaptation, … reinforcement learning. While other
surveys have focused on transfer learning techniques [18] or safe reinforcement learning [4], …

… He is passionate about popularizing artificial intelligence technologies and established
TensorLayer, a deep learning and reinforcement learning library for scientists and engineers, …

… Identification and definition of real-world challenges: Our main goal is to more clearly
define the issues reinforcement learning is having when dealing with real systems. By making …

Life-cycle production optimization aims to obtain the optimal well control scheme at each time
control step to maximize financial profit and hydrocarbon production. However, searching …

… • Identification and definition of real-world challenges: Our main goal is to more clearly
define the issues reinforcement learning is having when dealing with real systems. By making …

… We train two end-to-end, and 18 unsupervised-learningbased architectures, and compare
… working on a real life robot. Our results show that unsupervised learning methods are …

… A PID controller is integrated with PP by a customized reinforcement learning model to
better deal with tracking error by trading off between PP and PID. Moreover, a rough-to-fine …