142 citations · 356 across the 20 of their papers we have counts for
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Variable-Shot Adaptation for Online Meta-Learning
Tianhe Yu, Xinyang Geng, Chelsea Finn +1
Few-shot meta-learning methods consider the problem of learning new tasks from a small, fixed number of examples, by meta-learning across static data from a set of previous tasks.…
Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling
Russell Mendonca, Xinyang Geng, Chelsea Finn +1
Reinforcement learning algorithms can acquire policies for complex tasks autonomously. However, the number of samples required to learn a diverse set of skills can be prohibitively…
Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement
Benjamin Eysenbach, Xinyang Geng, Sergey Levine +1
Multi-task reinforcement learning (RL) aims to simultaneously learn policies for solving many tasks. Several prior works have found that relabeling past experience with different r…