Lin Guan

Explanation Augmented Feedback in Human-in-the-Loop Reinforcement Learning

2020 · arXiv (Cornell University)

Human-in-the-loop Reinforcement Learning (HRL) aims to integrate human guidance with Reinforcement Learning (RL) algorithms to improve sample efficiency and performance. A common type of human guidance in HRL is binary evaluative good or bad feedback …

Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity

2022 · arXiv (Cornell University)

Creating reinforcement learning (RL) agents that are capable of accepting and leveraging task-specific knowledge from humans has been long identified as a possible strategy for developing scalable approaches for solving long-horizon problems. While previous works …

VPFL: A Verifiable Property Federated Learning Framework Against Invisible Attacks in Distributed IoT

2023

The development of Federated Learning (FL) offers an efficient Machine Learning (ML) approach with privacy protection to solve the data island issue in distributed Internet of Things (IoT). However, existing FL frameworks still suffer from …