  Meta-reinforcement learning (meta-RL) acquires meta-policies that show good
performance for tasks in a wide task distribution. However, conventional
meta-RL, which learns meta-policies by randomly sampling tasks, has been
reported to show meta-overfitting for certain tasks, especially for easy tasks
where an agent can easily get high scores. To reduce effects of the
meta-overfitting, we considered meta-RL with curriculum-based task sampling.
Our method is Robust Meta Reinforcement Learning with Guided Task Sampling
(RMRL-GTS), which is an effective method that restricts task sampling based on
scores and epochs. We show that in order to achieve robust meta-RL, it is
necessary not only to intensively sample tasks with poor scores, but also to
restrict and expand the task regions of the tasks to be sampled.
