most citedRewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2022★ 2 cited

Rewards Encoding Environment Dynamics Improves Preference-based Reinforcement Learning

Katherine Metcalf, Miguel Sarabia, Barry-John Theobald

Preference-based reinforcement learning (RL) algorithms help avoid the pitfalls of hand-crafted reward functions by distilling them from human preference feedback, but they remain…

cs.CV2022

Contrastive Self-Supervised Learning for Skeleton Representations

Nico Lingg, Miguel Sarabia, Luca Zappella +1

Human skeleton point clouds are commonly used to automatically classify and predict the behaviour of others. In this paper, we use a contrastive self-supervised learning method, Si…