37 citations · 138 across the 17 of their papers we have counts for
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cs.LG2019★ 5 cited
Model Imitation for Model-Based Reinforcement Learning
Yueh-Hua Wu, Ting-Han Fan, Peter J. Ramadge +1
Model-based reinforcement learning (MBRL) aims to learn a dynamic model to reduce the number of interactions with real-world environments. However, due to estimation error, rollout…
cs.LG2019★ 10 cited
Multi-task Batch Reinforcement Learning with Metric Learning
Jiachen Li, Quan Vuong, Shuang Liu +5
We tackle the Multi-task Batch Reinforcement Learning problem. Given multiple datasets collected from different tasks, we train a multi-task policy to perform well in unseen tasks…
cs.LG2019★ 22 cited
How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?
Quan Vuong, Sharad Vikram, Hao Su +2
Recently, reinforcement learning (RL) algorithms have demonstrated remarkable success in learning complicated behaviors from minimally processed input. However, most of this succes…