2 citations · 4 across the 5 of their papers we have counts for
3 papers · 1 filter
Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs
Flint Xiaofeng Fan, Cheston Tan, Roger Wattenhofer +1
The rise of reinforcement learning (RL) in critical real-world applications demands a fundamental rethinking of privacy in AI systems. Traditional privacy frameworks, designed to p…
Robustness of Utilizing Feedback in Embodied Visual Navigation
Jenny Zhang, Samson Yu, Jiafei Duan +1
This paper presents a framework for training an agent to actively request help in object-goal navigation tasks, with feedback indicating the location of the target object in its fi…
FedHQL: Federated Heterogeneous Q-Learning
Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai +3
Federated Reinforcement Learning (FedRL) encourages distributed agents to learn collectively from each other's experience to improve their performance without exchanging their raw…