Q-Transformer: Scalable Offline Reinforcement Learning via
Autoregressive Q-Functions
- URL: http://arxiv.org/abs/2309.10150v2
- Date: Tue, 17 Oct 2023 07:00:46 GMT
- Title: Q-Transformer: Scalable Offline Reinforcement Learning via
Autoregressive Q-Functions
- Authors: Yevgen Chebotar, Quan Vuong, Alex Irpan, Karol Hausman, Fei Xia, Yao
Lu, Aviral Kumar, Tianhe Yu, Alexander Herzog, Karl Pertsch, Keerthana
Gopalakrishnan, Julian Ibarz, Ofir Nachum, Sumedh Sontakke, Grecia Salazar,
Huong T Tran, Jodilyn Peralta, Clayton Tan, Deeksha Manjunath, Jaspiar
Singht, Brianna Zitkovich, Tomas Jackson, Kanishka Rao, Chelsea Finn, Sergey
Levine
- Abstract summary: We present a scalable reinforcement learning method for training multi-task policies from large offline datasets.
Our method uses a Transformer to provide a scalable representation for Q-functions trained via offline temporal difference backups.
We show that Q-Transformer outperforms prior offline RL algorithms and imitation learning techniques on a large diverse real-world robotic manipulation task suite.
- Score: 143.89572689302497
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this work, we present a scalable reinforcement learning method for
training multi-task policies from large offline datasets that can leverage both
human demonstrations and autonomously collected data. Our method uses a
Transformer to provide a scalable representation for Q-functions trained via
offline temporal difference backups. We therefore refer to the method as
Q-Transformer. By discretizing each action dimension and representing the
Q-value of each action dimension as separate tokens, we can apply effective
high-capacity sequence modeling techniques for Q-learning. We present several
design decisions that enable good performance with offline RL training, and
show that Q-Transformer outperforms prior offline RL algorithms and imitation
learning techniques on a large diverse real-world robotic manipulation task
suite. The project's website and videos can be found at
https://qtransformer.github.io
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