Decision-making is a dynamic process requiring perception, memory, and
reasoning to make choices and find optimal policies. Traditional approaches to
decision-making suffer from sample efficiency and generalization, while
large-scale self-supervised pretraining has enabled fast adaptation with
fine-tuning or few-shot learning in language and vision. We thus argue to
integrate knowledge acquired from generic large-scale self-supervised
pretraining into downstream decision-making problems. We propose
Pretrain-Then-Adapt pipeline and survey recent work on data collection,
pretraining objectives and adaptation strategies for decision-making
pretraining and downstream inference. Finally, we identify critical challenges
and future directions for developing decision foundation model with the help of
generic and flexible self-supervised pretraining.

The Integration of Self-Supervised Pretraining with Decision-Making: A Multi-Disciplinary Approach

Decision-making is a complex process that encompasses several cognitive abilities such as perception, memory, and reasoning. Traditionally, decision-making approaches have faced limitations in terms of sample efficiency and generalization. However, recent advancements in large-scale self-supervised pretraining have demonstrated its potential in enabling fast adaptation through techniques like fine-tuning and few-shot learning in language and vision domains. Recognizing the benefits of such pretraining, we advocate for the integration of knowledge acquired from generic large-scale self-supervised pretraining into downstream decision-making problems.

To facilitate this integration, we propose the Pretrain-Then-Adapt pipeline, which serves as a framework for incorporating self-supervised pretraining into decision-making tasks. This pipeline involves three main stages: data collection, pretraining objectives, and adaptation strategies.

Data Collection

In order to effectively pretrain decision foundation models, it is crucial to collect diverse and representative data that captures different aspects of decision-making. This may involve gathering real-world datasets or creating synthetic environments to simulate various scenarios. By ensuring the inclusion of different contexts and challenges, the resulting decision foundation models can better generalize across diverse decision-making situations.

Pretraining Objectives

During the pretraining phase, it is essential to define suitable objectives that allow the model to learn meaningful representations of decision-related information. These objectives should capture key properties such as causality, temporal relationships, uncertainty, and contextual dependencies. By incorporating these objectives into the pretraining process, decision foundation models can acquire a rich knowledge base that aids in subsequent downstream tasks.

Adaptation Strategies

Once the decision foundation model has been pretrained, it needs to be fine-tuned or adapted to specific decision-making problems. This can be achieved through techniques like transfer learning, where the model leverages the knowledge acquired during pretraining to quickly adapt to new situations with limited labeled data. Few-shot learning approaches can also be employed, allowing the model to make effective decisions even when only a small amount of labeled data is available.

By employing this Pretrain-Then-Adapt pipeline, researchers and practitioners can harness the power of self-supervised pretraining to enhance decision-making processes. This multi-disciplinary approach combines techniques from fields such as cognitive science, machine learning, and artificial intelligence, highlighting the diverse nature of concepts involved in decision-making.

In conclusion, integrating knowledge acquired from generic large-scale self-supervised pretraining into decision-making tasks offers promising opportunities for improving sample efficiency and generalization. The Pretrain-Then-Adapt pipeline provides a systematic framework for incorporating self-supervised pretraining into decision-making workflows. However, there are still critical challenges to address, such as designing effective pretraining objectives, handling bias in collected data, and ensuring robust adaptation strategies. Future research should focus on overcoming these challenges to develop decision foundation models that leverage the benefits of self-supervised pretraining in a more generic and flexible manner.

Read the original article