339 citations · 423 across the 9 of their papers we have counts for
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cs.LG2019★ 339 cited
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He +7
Meta-reinforcement learning algorithms can enable robots to acquire new skills much more quickly, by leveraging prior experience to learn how to learn. However, much of the current…
cs.LG2018
Scaling simulation-to-real transfer by learning composable robot skills
Ryan Julian, Eric Heiden, Zhanpeng He +5
We present a novel solution to the problem of simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simu…