IEEE

Heterogeneous Graph Reinforcement Learning for Dependency-aware Multi-task Allocation in Spatial Crowdsourcing

Abstract: Spatial Crowdsourcing (SC) has emerged as a significant paradigm for executing complex real-world projects and tasks, which are often decomposed into interdependent subtasks requiring ...
techxplore

Why reinforcement learning breaks at scale, and how a new method fixes it

From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have an important impact. That may feel especially true, for example, when ...
GitHub

A comprehensive implementation of self-supervised reinforcement learning algorithms including contrastive learning, predictive models, and representation learning for RL tasks.

Self-supervised reinforcement learning is a technique where agents learn useful representations and skills from the environment through self-generated tasks, such as predicting next states or learning ...
IEEE

RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks

Abstract: As large-scale distributed energy resources are integrated into the active distribution networks (ADNs), effective energy management in ADNs becomes increasingly prominent compared to ...