Reinforcement Learning with Videos: Combining Offline Observations with
Interaction
- URL: http://arxiv.org/abs/2011.06507v2
- Date: Thu, 4 Nov 2021 20:07:57 GMT
- Title: Reinforcement Learning with Videos: Combining Offline Observations with
Interaction
- Authors: Karl Schmeckpeper, Oleh Rybkin, Kostas Daniilidis, Sergey Levine,
Chelsea Finn
- Abstract summary: Reinforcement learning is a powerful framework for robots to acquire skills from experience.
Videos of humans are a readily available source of broad and interesting experiences.
We propose a framework for reinforcement learning with videos.
- Score: 151.73346150068866
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Reinforcement learning is a powerful framework for robots to acquire skills
from experience, but often requires a substantial amount of online data
collection. As a result, it is difficult to collect sufficiently diverse
experiences that are needed for robots to generalize broadly. Videos of humans,
on the other hand, are a readily available source of broad and interesting
experiences. In this paper, we consider the question: can we perform
reinforcement learning directly on experience collected by humans? This problem
is particularly difficult, as such videos are not annotated with actions and
exhibit substantial visual domain shift relative to the robot's embodiment. To
address these challenges, we propose a framework for reinforcement learning
with videos (RLV). RLV learns a policy and value function using experience
collected by humans in combination with data collected by robots. In our
experiments, we find that RLV is able to leverage such videos to learn
challenging vision-based skills with less than half as many samples as RL
methods that learn from scratch.
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