Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …

Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision …

Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision …

Interactive 3D simulations have enabled break-throughs in robotics and computer vision, but
simulating the broad diversity of environments needed for deep learning requires large …

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …

Deep reinforcement learning has recently seen huge success across multiple areas in the
robotics domain. Owing to the limitations of gathering real-world data, ie, sample inefficiency …

In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data
from the environment, transmit them to cloud centers, and receive feedback via the Internet …

Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …

With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …