Robust Meta-Reinforcement Learning with Curriculum-Based Task Sampling

• URL: http://arxiv.org/abs/2203.16801v1
• Date: Thu, 31 Mar 2022 05:16:24 GMT
• Title: Robust Meta-Reinforcement Learning with Curriculum-Based Task Sampling
• Authors: Morio Matsumoto, Hiroya Matsuba, and Toshihiro Kujirai
• Abstract summary: We show that Robust Meta Reinforcement Learning with Guided Task Sampling (RMRL-GTS) is an effective method that restricts task sampling based on scores and epochs. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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