The field of vision modeling and power grid control is witnessing significant advancements with the integration of state-space models (SSMs) and reinforcement learning (RL) techniques. Researchers are exploring the potential of SSMs, particularly the Mamba architecture, to improve the efficiency and accuracy of vision modeling tasks such as image generation and optical flow estimation. Meanwhile, in the domain of power grid control, RL is being applied to optimize energy management and restoration of power distribution systems. Noteworthy papers in this area include Arcee, which proposes a differentiable recurrent state chain for generative vision modeling, and Heterogeneous Multi-Agent Proximal Policy Optimization for Power Distribution System Restoration, which applies a heterogeneous-agent RL framework to enable coordinated restoration across interconnected microgrids. These innovative approaches are advancing the field and paving the way for more efficient and resilient power grid control systems.
Advancements in Vision Modeling and Power Grid Control
Sources
Arcee: Differentiable Recurrent State Chain for Generative Vision Modeling with Mamba SSMs
DensePercept-NCSSD: Vision Mamba towards Real-time Dense Visual Perception with Non-Causal State Space Duality
X-VMamba: Explainable Vision Mamba
Wide-Area Feedback Control for Renewables-Heavy Power Systems: A Comparative Study of Reinforcement Learning and Lyapunov-Based Design
From Black-Box to White-Box: Control-Theoretic Neural Network Interpretability
A Receding Horizon Reinforcement Learning Framework for Campus Chiller Energy Management - A case study from an Australian University
Heterogeneous Multi-Agent Proximal Policy Optimization for Power Distribution System Restoration
An Interpretable Federated Learning Control Framework Design for Smart Grid Resilience