6 citations · 13 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 5 cited
Deep Black-Box Reinforcement Learning with Movement Primitives
Fabian Otto, Onur Celik, Hongyi Zhou +3
\Episode-based reinforcement learning (ERL) algorithms treat reinforcement learning (RL) as a black-box optimization problem where we learn to select a parameter vector of a contro…
cs.RO2022★ 2 cited
ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives
Ge Li, Zeqi Jin, Michael Volpp +3
Movement Primitives (MPs) are a well-known concept to represent and generate modular trajectories. MPs can be broadly categorized into two types: (a) dynamics-based approaches that…
cs.LG2021★ 6 cited
Differentiable Trust Region Layers for Deep Reinforcement Learning
Fabian Otto, Philipp Becker, Ngo Anh Vien +2
Trust region methods are a popular tool in reinforcement learning as they yield robust policy updates in continuous and discrete action spaces. However, enforcing such trust region…