  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
