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The success of transformer models trained with a language modeling objectivebrings a promising opportunity to the reinforcement learning framework.Decision Transformer is a step towards this direction, showing how to traintransformers with a similar next-step prediction objective on offline data.Another important development in this area is the recent emergence oflarge-scale datasets collected from the internet, such as the ones composed oftutorial videos with captions where people talk about what they are doing. Totake advantage of this language component, we propose a novel method forunifying language reasoning with actions in a single policy. Specifically, weaugment a transformer policy with word outputs, so it can generate textualcaptions interleaved with actions. When tested on the most challenging task inBabyAI, with captions describing next subgoals, our reasoning policyconsistently outperforms the caption-free baseline.

Think Before You Act: Unified Policy for Interleaving Language Reasoning with Actions