356 citations · 2.1k across the 32 of their papers we have counts for
3 papers · 1 filter
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
Lantao Yu, Tianhe Yu, Chelsea Finn +1
Providing a suitable reward function to reinforcement learning can be difficult in many real world applications. While inverse reinforcement learning (IRL) holds promise for automa…
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…
Unsupervised Visuomotor Control through Distributional Planning Networks
Tianhe Yu, Gleb Shevchuk, Dorsa Sadigh +1
While reinforcement learning (RL) has the potential to enable robots to autonomously acquire a wide range of skills, in practice, RL usually requires manual, per-task engineering o…