SAD: State-Action Distillation for In-Context Reinforcement Learning under Random Policies

Pretrained foundation models have exhibited extraordinary in-context learning performance, allowing zero-shot generalization to new tasks not encountered during the pretraining. In the case of RL, in-context RL (ICRL) emerges when pretraining FMs on decision-making problems in an autoregressive-supervised manner. Nevertheless, current state-of-the-art ICRL algorithms, such as AD, DPT and DIT, impose stringent requirements on generating the pretraining dataset concerning the behavior (source) policies, context information, and action labels, etc. Notably, these algorithms either demand optimal policies or require varying degrees of well-trained behavior policies for all environments during the generation of the pretraining dataset. This significantly hinders the application of ICRL to real-world scenarios, where acquiring optimal or well-trained policies for a substantial volume of real-world training environments can be both prohibitively intractable and expensive. To overcome this challenge, we introduce a novel approach, termed State-Action Distillation (SAD), that allows to generate a remarkable pretraining dataset guided solely by random policies. In particular, SAD selects query states and corresponding action labels by distilling the outstanding state-action pairs from the entire state and action spaces by using random policies within a trust horizon, and then inherits the classical autoregressive-supervised mechanism during the pretraining. To the best of our knowledge, this is the first work that enables promising ICRL under (e.g., uniform) random policies and random contexts. We establish theoretical analyses regarding the performance guarantees of SAD. Moreover, our empirical results across multiple ICRL benchmark environments demonstrate that, on average, SAD outperforms the best baseline by 180.86% in the offline evaluation and by 172.8% in the online evaluation.