Corpus ID: 231592626

Linear Representation Meta-Reinforcement Learning for Instant Adaptation

@article{Peng2021LinearRM,
  title={Linear Representation Meta-Reinforcement Learning for Instant Adaptation},
  author={Matt Peng and Banghua Zhu and Jiantao Jiao},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.04750}
}
This paper introduces Fast Linearized Adaptive Policy (FLAP), a new metareinforcement learning (meta-RL) method that is able to extrapolate well to outof-distribution tasks without the need to reuse data from training, and adapt almost instantaneously with the need of only a few samples during testing. FLAP builds upon the idea of learning a shared linear representation of the policy so that when adapting to a new task, it suffices to predict a set of linear weights. A separate adapter network… Expand
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