Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Understand

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.