What is the degradation in success rate for meta-reinforcement learning policies trained on narrow task distri

Abstract

Abstract: Meta-reinforcement learning algorithms can enable robots to acquire new skills much more quickly, by leveraging prior experience to learn how to learn. However, much of the current research on meta-reinforcement learning focuses on task distributions that are very narrow. For example, a commonly used meta-reinforcement learning benchmark uses different running velocities for a simulated robot as different tasks. When policies are meta-trained on such narrow task distributions, they cannot possibly generalize to more quickly acquire entirely new tasks. Therefore, if the aim of these methods is

Research Question

What is the degradation in success rate for meta-reinforcement learning policies trained on narrow task distributions like Meta-World when transferred to diverse robotic manipulation domains?

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