Electrified vehicles provide an effective solution to address the unfavorable impacts of fossil
fuel use in the transportation sector. Energy management strategy (EMS) is the core …

The total capital in cryptocurrency markets is around two trillion dollars in 2022, which is
almost the same as Apple's market capitalisation at the same time. Increasingly …

Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-
critic methods perform poorly due to the overestimation of out-of-distribution state-action …

Owing to the fact that the number and complexity of machines is increasing in Industry 4.0,
the maintenance process is more time-consuming and labor-intensive, which contains …

In this article, a novel reinforcement learning (RL) approach, continuous dynamic policy
programming (CDPP), is proposed to tackle the issues of both learning stability and sample …

Abstract Proximal Policy Optimization (PPO) is an important reinforcement learning method,
which has achieved great success in sequential decision-making problems. However, PPO …

Time-sensitive applications, eg, Internet of vehicles applications and tactile Internet
applications, put forward low latency and high throughput requirements for communication …

Wastewater treatment plants (WWTPs) increasingly utilize sensors to optimize operations
and ensure treated water quality. These sensors' rich datasets are well-suited for automated …

In safety-critical robotic tasks, potential failures must be reduced, and multiple constraints
must be met, such as avoiding collisions, limiting energy consumption, and maintaining …

This letter aims to solve a safe reinforcement learning (RL) problem with risk measure-based
constraints. As risk measures, such as conditional value at risk (CVaR), focus on the tail …