34 citations · 59 across the 9 of their papers we have counts for
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cs.LG2023★ 1 cited
On Context Distribution Shift in Task Representation Learning for Offline Meta RL
Chenyang Zhao, Zihao Zhou, Bin Liu
Offline Meta Reinforcement Learning (OMRL) aims to learn transferable knowledge from offline datasets to enhance the learning process for new target tasks. Context-based Reinforcem…
cs.LG2020★ 5 cited
Robust Domain Randomised Reinforcement Learning through Peer-to-Peer Distillation
Chenyang Zhao, Timothy Hospedales
In reinforcement learning, domain randomisation is an increasingly popular technique for learning more general policies that are robust to domain-shifts at deployment. However, nai…
cs.LG2019★ 34 cited
Investigating Generalisation in Continuous Deep Reinforcement Learning
Chenyang Zhao, Olivier Sigaud, Freek Stulp +1
Deep Reinforcement Learning has shown great success in a variety of control tasks. However, it is unclear how close we are to the vision of putting Deep RL into practice to solve r…