In deep reinforcement learning, off-policy data help reduce on-policy interaction with the
environment, and the trust region policy optimization (TRPO) method is efficient to stabilize …

Integrating distributed solar fields (DSFs) into conventional heat and power plants (CHPs) of
industries is mostly constrained by the availability of a real-time capable control scheme …

The propagation of distributed renewable energy resources poses several challenges in the
operation of microgrids due to uncertainty. In traditional energy scheduling approaches, the …

Due to the negative impedance potential of constant power loads (CPLs), the stability of
power electronic converters-based electrical distribution networks is prone to instability. This …

In this article, the trajectory planning of the two manipulators of the dual-arm robot is studied
to approach the patient in a complex environment with deep reinforcement learning …

This paper proposes the use of unmanned aerial vehicles (UAVs) with intelligent reflecting
surfaces (IRS) to reflect signals from the industrial Internet of things (IIoT) to the destination …

This paper extends a novel model for modern flexible manufacturing systems: the multi-
objective dynamic partial-re-entrant hybrid flow shop scheduling problem (MDPR-HFSP) …

The introduction of network function virtualization (NFV) leads to service function chain
(SFC) deployment problems, promoting the idea of composing network services as …

Previous research on automatic control of high-speed trains in speed limit sections is
insufficient. This article proposes a new offline reinforcement learning strategy for automatic …

In the field of intelligent anti-jamming, deep reinforcement learning algorithms are regarded
as key technical means. However, the learning process of deep reinforcement learning …