  Meta-reinforcement learning (RL) addresses the problem of sample inefficiency
in deep RL by using experience obtained in past tasks for a new task to be
solved.
  However, most meta-RL methods require partially or fully on-policy data,
i.e., they cannot reuse the data collected by past policies, which hinders the
improvement of sample efficiency.
  To alleviate this problem, we propose a novel off-policy meta-RL method,
embedding learning and evaluation of uncertainty (ELUE).
  An ELUE agent is characterized by the learning of a feature embedding space
shared among tasks.
  It learns a belief model over the embedding space and a belief-conditional
policy and Q-function.
  Then, for a new task, it collects data by the pretrained policy, and updates
its belief based on the belief model.
  Thanks to the belief update, the performance can be improved with a small
amount of data.
  In addition, it updates the parameters of the neural networks to adjust the
pretrained relationships when there are enough data.
  We demonstrate that ELUE outperforms state-of-the-art meta RL methods through
experiments on meta-RL benchmarks.
