arXiv subjects

Haoyu Yan

Publications and source records attributed to Haoyu Yan.

4 recordsLinked to original sources

arXiv2026

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling

The rapid proliferation of Electric Vehicles (EVs) introduces significant spatio-temporal uncertainties into power grids, while Vehicle-to-Grid (V2G) technology offers critical flexibility through bidirectional power flow. However, integrating large-scale EVs into the Optimal Power Flow framework presents substantial challenges due to computational bottlenecks arising from solver complexity and coupled spatio-temporal constraints. Existing Reinforcement Learning (RL) methods often struggle to balance strict constraint satisfaction with scalability in highly dynamic EV fleet environments. To address these challenges, this paper proposes a Hierarchical Policy for Constrained Reinforcement Learning (HPC-RL) framework for spatially and temporally coupled V2G scheduling. The framework adopts a two-layer architecture: the upper level utilizes a RL algorithm based on the Generalized Reduced Gradient method to strictly enforce spatial grid-level hard constraints; the lower level implements a novel dynamic boundary strategy to compute real-time feasible charging power bounds for individual EVs, thereby ensuring the satisfaction of temporal charging demands. This integrated design not only enables the simultaneous handling of spatially and temporally coupled constraints during the RL optimization process but also significantly enhances generalization capabilities for large-scale fleets through hierarchical decoupling. Extensive experiments on IEEE 14, 30, and modified 141-bus systems demonstrate that HPC-RL outperforms Model Predictive Control and state-of-the-art safe RL baselines across all metrics. The proposed method achieves near-optimal scheduling strategies and drastically reduces online computation time in large-scale scenarios from hours to minutes, while maintaining a near-zero constraint violation rate and nearly 100\% charging demand satisfaction.

cs.CE↗

arXiv2026

Unsupervised Learning for AC Optimal Power Flow with Fast Physics-Aware Layer

Learning to solve the Alternating Current Optimal Power Flow (AC-OPF) problem by neural networks (NNs) is a promising approach in real-time applications. Existing methods to ensure the physical feasibility of NN outputs embed a power flow (PF) solver within networks. However, the gradient through the PF solver, namely, implicit differentiation, needs manual Jacobian derivation and the solution of linear systems, which is computationally prohibitive and hinders integration with modern automatic differentiation (AD) frameworks. To address these challenges, we propose FPL-OPF, a novel unsupervised learning framework that incorporates a Fast Physics-aware Layer for AC-OPF problems. FPL-OPF embeds a fast PF iterative solver within the NN and takes solely the last few or even the final iterations into the AD graph. This design ensures high computational efficiency for both the forward and backward passes, circumventing complex custom backward implementations. Theoretically, we rigorously prove that the gradient from this design serves as a high-fidelity surrogate of the true implicit gradient under mild conditions. Extensive experiments demonstrate that FPL-OPF achieves significant speedups over state-of-the-art unsupervised learning approaches, while maintaining near-zero constraint violations and competitive optimality. Our code is available at https://github.com/wowotou1998/fpl-opf

cs.CE↗

arXiv2026

Construction of the Global $\chi^2$ Function for the Simultaneous Fitting of Correlated Energy-Dependent Cross Sections

In this paper, the global $\chi^2$ function for the simultaneous fitting of correlated energy-dependent cross sections is constructed, where the correlations between the measured cross sections of different processes and/or at different center-of-mass energy points, as well as the contributions from the integrated luminosity measurement and the center-of-mass energy measurement, are taken into account.

physics.data-an↗

arXiv2025

HOID-R1: Reinforcement Learning for Open-World Human-Object Interaction Detection Reasoning with Multimodal Large Language Model

Understanding and recognizing human-object interaction (HOI) is a pivotal application in AR/VR and robotics. Recent open-vocabulary HOI detection approaches depend exclusively on large language models for richer textual prompts, neglecting their inherent 3D spatial understanding capabilities. To address this shortcoming, we introduce HOID-R1, the first HOI detection framework that integrates chain-of-thought (CoT) guided supervised fine-tuning (SFT) with group relative policy optimization (GRPO) within a reinforcement learning (RL) paradigm. Specifically, we initially apply SFT to imbue the model with essential reasoning capabilities, forcing the model to articulate its thought process in the output. Subsequently, we integrate GRPO to leverage multi-reward signals for policy optimization, thereby enhancing alignment across diverse modalities. To mitigate hallucinations in the CoT reasoning, we introduce an "MLLM-as-a-judge" mechanism that supervises the CoT outputs, further improving generalization. Extensive experiments show that HOID-R1 achieves state-of-the-art performance on HOI detection benchmarks and outperforms existing methods in open-world generalization to novel scenarios.

cs.CV↗