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Supervised Pretraining Can Learn In-Context Reinforcement Learning (2306.14892v1)

Published 26 Jun 2023 in cs.LG and cs.AI

Abstract: Large transformer models trained on diverse datasets have shown a remarkable ability to learn in-context, achieving high few-shot performance on tasks they were not explicitly trained to solve. In this paper, we study the in-context learning capabilities of transformers in decision-making problems, i.e., reinforcement learning (RL) for bandits and Markov decision processes. To do so, we introduce and study Decision-Pretrained Transformer (DPT), a supervised pretraining method where the transformer predicts an optimal action given a query state and an in-context dataset of interactions, across a diverse set of tasks. This procedure, while simple, produces a model with several surprising capabilities. We find that the pretrained transformer can be used to solve a range of RL problems in-context, exhibiting both exploration online and conservatism offline, despite not being explicitly trained to do so. The model also generalizes beyond the pretraining distribution to new tasks and automatically adapts its decision-making strategies to unknown structure. Theoretically, we show DPT can be viewed as an efficient implementation of Bayesian posterior sampling, a provably sample-efficient RL algorithm. We further leverage this connection to provide guarantees on the regret of the in-context algorithm yielded by DPT, and prove that it can learn faster than algorithms used to generate the pretraining data. These results suggest a promising yet simple path towards instilling strong in-context decision-making abilities in transformers.

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Authors (7)
  1. Jonathan N. Lee (11 papers)
  2. Annie Xie (21 papers)
  3. Aldo Pacchiano (72 papers)
  4. Yash Chandak (32 papers)
  5. Chelsea Finn (264 papers)
  6. Ofir Nachum (64 papers)
  7. Emma Brunskill (86 papers)
Citations (56)
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Summary

Analyzing Supervised Pretraining for In-Context Reinforcement Learning

The paper "Supervised Pretraining Can Learn In-Context Reinforcement Learning" tackles the challenge of applying in-context learning capabilities of transformer models to reinforcement learning (RL), specifically in decision-making problems such as bandits and Markov decision processes (MDPs). The paper introduces a novel approach, the Decision-Pretrained Transformer (DPT), aimed at leveraging diverse datasets to predict optimal actions within unknown RL tasks. This methodology presents a model that adapts well to both online exploration and offline conservatism, showing potential beyond its pretraining sources.

Key Findings

  1. Near-Optimal Decision Making: The DPT primarily predicts optimal actions based on given in-context interactions. Despite the simplicity of the objective, the model emerges as a proficient decision-maker even under uncertainty during test-time tasks. Notably, DPT exhibits competent exploratory strategies.
  2. Generalization Across Tasks: The model's training exhibits robustness, adequately functioning in bandit problems with unseen reward distributions, and adapting to unseen goals and dynamics in simple MDPs. DPT implements adaptive decision-making strategies that accommodate unknown structures.
  3. Leveraging Task Structure: Importantly, DPT proves capable of enhancing decision-making processes, surpassing the efficacy of algorithms used in pretraining interactions. For example, when faced with parametric bandit problems, DPT discernibly exploits latent linear structures to improve regret bounds, performing on par with specialized algorithms designed with prior structural knowledge.
  4. Empirical Implementation of Posterior Sampling: The paper theoretically aligns DPT’s mechanism with Bayesian posterior sampling—a known sample-efficient RL algorithm—demonstrating that DPT effectively acts as an implementation of posterior sampling, which historically suffers from computational burdens.

Implications and Future Directions

This research offers promising implications for practical and theoretical advancements in in-context learning within RL frameworks. Practically, DPT represents a strategy that efficiently amalgamates exploration and exploitation, beneficial for fields like robotics and recommendation systems. Theoretically, it indicates that pretraining transformers in context settings can inherently instill decision-making capabilities, advancing toward computationally viable Bayesian methods in RL.

However, challenges remain: optimizing the model for varying task domains, addressing distributional shifts, and expanding the utility of pretraining with non-optimal action labels are pathways demanding further exploration. Moreover, future work can broaden understanding of how existing foundation models, particularly those finetuned for instructional utility, might incorporate DPT strategies for enhanced decision-making abilities.

Conclusion

Overall, the paper provides comprehensive insights into the intersection of in-context learning and reinforcement learning, harnessing the architectural capabilities of transformers for robust, adaptable decision-making. The findings highlight a sophisticated yet straightforward path in leveraging supervised pretraining to extend RL capabilities, offering a fertile ground for future exploration and development in artificial intelligence research.

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