  A longstanding goal of the field of AI is a method for learning a highly
capable, generalist agent from diverse experience. In the subfields of vision
and language, this was largely achieved by scaling up transformer-based models
and training them on large, diverse datasets. Motivated by this progress, we
investigate whether the same strategy can be used to produce generalist
reinforcement learning agents. Specifically, we show that a single
transformer-based model - with a single set of weights - trained purely offline
can play a suite of up to 46 Atari games simultaneously at close-to-human
performance. When trained and evaluated appropriately, we find that the same
trends observed in language and vision hold, including scaling of performance
with model size and rapid adaptation to new games via fine-tuning. We compare
several approaches in this multi-game setting, such as online and offline RL
methods and behavioral cloning, and find that our Multi-Game Decision
Transformer models offer the best scalability and performance. We release the
pre-trained models and code to encourage further research in this direction.
