reinforcement learning

Deep Reinforcement Learning-Based Coordinated Voltage Control of HST and DG

Findings indicate that deep reinforcement learning provides an effective optimization approach for coordinated HST and DG control and offers a useful reference for voltage regulation in active distribution networks.

THE IMPACT OF DIGITAL TRANSFORMATION ON ORGANIZATIONAL RESILIENCE: EVIDENCE FROM SMES IN VIETNAM

The findings reveal that digital transformation positively and directly enhances organizational resilience, and a complementary relationship between learning and innovation is uncovered, highlighting their mutual reinforcement in strengthening firms’ ability to withstand disruption.

Dynamics-aware hierarchical decision–control with egocentric perception for robust navigation of underactuated uncrewed surface vehicles

Reliable navigation of underactuated uncrewed surface vehicles (USVs) in complex waters depends on the consistency between adaptive decision-making and physically executable motion under nonlinear dynamics and environmental disturbances. Existing deep reinforcement learning methods often insufficiently account for the...

Adaptive Scheduling of Public Electric Vehicle Fast-Charging Stations Based on State-Aware Multi-Agent Reinforcement Learning

Empirical comparisons show that PUI-MAPPO (multi-agent proximal policy optimization) achieves the best performance among all PUI-enhanced variants, and ablation studies further validate the individual effectiveness of the PUI urgency mechanism, the dynamic threshold framework, and the adaptive reward function.

Variational autoencoder-based robust learning control for nonlinear systems with piecewise disturbances

This paper proposes an adaptive reinforcement learning framework based on a Variational Autoencoder (VAE) for robust control of nonlinear systems subject to piecewise disturbances. The framework addresses incomplete state information in partially observable environments by augmenting states with inferred latent disturb...

Energy management for hydrogen refueling stations: A synergistic approach of cascade compression architecture and deep reinforcement learning

With the growth of the fuel cell electric vehicles market, hydrogen refueling stations have become vital infrastructure bridging hydrogen production and dispensing. In hydrogen refueling stations, the compression process is a necessary and the most energy-intensive stage requiring further optimization aimed at reducing...

Deep reinforcement learning-based position optimization model for intermediate discharge valves in a scroll compressor

Heating and cooling cycles can be controlled by changing the suction and discharge pressures of a scroll compressor. Over-pressurization caused by a low compression ratio induces work loss and may damage the structure of the scroll. This over-pressurization can be prevented by discharging compressed refrigerant through...

Integrating Dynamic Graph Representation and Reinforcement Learning for Portfolio Optimization

The complex dependency relationships and high-frequency, time-varying characteristics of financial market data pose many challenges to portfolio optimization. To address these problems, this paper proposes a robust portfolio optimization framework—dynamic graph reinforcement learning (DyGRL)—by integrating dynamic grap...

Continuous Reward-Driven Learning in Spiking Neural Networks for Embedded Event-Based Robot Control

A Reinforcement Learning Supervisor with Dynamic Performance-Metric Weighting for Cryptocurrency Portfolio Management

Uncertainty-aware model-enhanced soft actor-critic for conformal coating parameter optimization

Conformal coating optimization requires control of multiple application parameters to achieve target thickness and coverage uniformity, with spray atomization settings (atomization valve control) playing a crucial role in droplet formation and material deposition. Traditional manual tuning yields a low success rate...

Comprehensive Chemical Space Exploration in De Novo Drug Design: An Algorithmically Constrained, Parallel Dual-Paradigm Framework Validated on BACE1

Isolated de novo drug design paradigms carry inherent vulnerabilities: ligand-based (LBDD) models risk unreliable extrapolation beyond their training domain, while structure-based (SBDD) genetic algorithms accumulate molecular size-bias across evolutionary generations. Furthermore, on structurally complex targets, depl...