36 citations · 122 across the 11 of their papers we have counts for
Showing 2019Show all
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cs.AI2019★ 3 cited
Towards More Sample Efficiency in Reinforcement Learning with Data Augmentation
Yijiong Lin, Jiancong Huang, Matthieu Zimmer +2
Deep reinforcement learning (DRL) is a promising approach for adaptive robot control, but its current application to robotics is currently hindered by high sample requirements. We…
cs.RO2019★ 36 cited
Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement Learning
Yijiong Lin, Jiancong Huang, Matthieu Zimmer +3
Deep Reinforcement Learning (RL) is a promising approach for adaptive robot control, but its current application to robotics is currently hindered by high sample requirements. To a…
cs.LG2019★ 7 cited
Exploiting the Sign of the Advantage Function to Learn Deterministic Policies in Continuous Domains
Matthieu Zimmer, Paul Weng
In the context of learning deterministic policies in continuous domains, we revisit an approach, which was first proposed in Continuous Actor Critic Learning Automaton (CACLA) and…