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Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning by Gupta, Abhishek; Kumar, Vikash; Lynch, Corey; Levine, Sergey; Hausman, Karol is a scholarly article available to read on EtoBox.

What is Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning about?

We present relay policy learning, a method for imitation and reinforcement learning that can solve multi-stage, long-horizon robotic tasks. This general and universally-applicable, two-phase approach consists of an imitation learning stage that produces goal-conditioned hierarchical policies, and a reinforcement learning phase that finetunes these policies for task performance. Our method, while not necessarily perfect at imitation learning, is very amenable to further improvement via environment interaction, allowing it to scale to challenging long-horizon tasks. We simplify the long-horizon policy learning problem by using a novel data-relabeling algorithm for learning goal-conditioned hierarchical policies, where the low-level only acts for a fixed number of steps, regardless of the goal achieved. While we rely on demonstration data to bootstrap policy learning, we do not assume access to demonstrations of every specific tasks that is being solved, and instead leverage unstructured and unsegmented demonstrations of semantically meaningful behaviors that are not only less burdensome to provide, but also can greatly facilitate further improvement using reinforcement learning. We d

Author

Gupta, Abhishek; Kumar, Vikash; Lynch, Corey; Levine, Sergey; Hausman, Karol

Published

2019

Language

EN