The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

Underwater images captured in diverse underwater scenes exhibit varying types and
degrees of degradation, including color deviations, low contrast, blurry details, etc. Single …

Despite the potential of reinforcement learning (RL) for building general-purpose robotic
systems, training RL agents to solve robotics tasks still remains challenging due to the …

Realistic manipulation tasks require a robot to interact with an environment with a prolonged
sequence of motor actions. While deep reinforcement learning methods have recently …

In recent years, the importance of electric mobility has increased in response to climate
change. The fast-growing deployment of electric vehicles (EVs) worldwide is expected to …

The wide-spread embracement and integration of Internet of Things (IoT) has inevitably lead
to an explosion in the number of IoT devices. This in turn has led to the generation of …

Existing power system resilience enhancement methods, such as proactive generation
rescheduling, movable sources dispatch, and network topology reconfiguration, do not …

We present C· ASE, an efficient and effective framework that learns Conditional Adversarial
Skill Embeddings for physics-based characters. C· ASE enables the physically simulated …

Purpose-This study aims to propose an enhanced eco-driving strategy based on
reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the …

In responseto low-carbon requirements, a large amount of renewable energy resources
(RESs) have been deployed in power systems; nevertheless, the intermittency of RESs …