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Meta-reinforcement learning (meta-RL) acquires meta-policies that show goodperformance for tasks in a wide task distribution. However, conventionalmeta-RL, which learns meta-policies by randomly sampling tasks, has beenreported to show meta-overfitting for certain tasks, especially for easy taskswhere an agent can easily get high scores. To reduce effects of themeta-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 onscores and epochs. We show that in order to achieve robust meta-RL, it isnecessary not only to intensively sample tasks with poor scores, but also torestrict and expand the task regions of the tasks to be sampled.

Robust Meta-Reinforcement Learning with Curriculum-Based Task Sampling