Search Results for author:
Found 3 papers, 2 papers with code
From Model-Based to Model-Free: Learning Building Control for Demand Response
We demonstrate, in this context, that hybrid methods offer many benefits over both purely model-free and model-based methods as long as certain requirements are met.
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems
We present the PowerGridworld software package to provide users with a lightweight, modular, and customizable framework for creating power-systems-focused, multi-agent Gym environments that readily integrate with existing training frameworks for reinforcement learning (RL).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning-Accelerated ADMM for Distributed Optimal Power Flow
We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions.
