Multi-Game Decision Transformers

Understand

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.