IEEE

Solving Robotic Manipulation With Sparse Reward Reinforcement Learning Via Graph-Based Diversity and Proximity

Abstract: In multigoal reinforcement learning (RL), algorithms usually suffer from inefficiency in the collection of successful experiences in tasks with sparse rewards. By utilizing the ideas of ...
IEEE

Solving AC Power Flow with Graph Neural Networks under Realistic Constraints

Abstract: In this paper, we propose a graph neural network architecture to solve the AC power flow problem under realistic constraints. To ensure a safe and resilient operation of distribution grids, ...
GitHub

PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network

This repo is the official implementation of "PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network" by Longxiang Jiang, Liyuan Wang, Xinkun Chu, Yonghao Xiao, and Hao Zhang ...