Emergent Mind

Abstract

We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks. Premier-TACO leverages a subset of multitask offline datasets for pretraining a general feature representation, which captures critical environmental dynamics and is fine-tuned using minimal expert demonstrations. It advances the temporal action contrastive learning (TACO) objective, known for state-of-the-art results in visual control tasks, by incorporating a novel negative example sampling strategy. This strategy is crucial in significantly boosting TACO's computational efficiency, making large-scale multitask offline pretraining feasible. Our extensive empirical evaluation in a diverse set of continuous control benchmarks including Deepmind Control Suite, MetaWorld, and LIBERO demonstrate Premier-TACO's effectiveness in pretraining visual representations, significantly enhancing few-shot imitation learning of novel tasks. Our code, pretraining data, as well as pretrained model checkpoints will be released at https://github.com/PremierTACO/premier-taco. Our project webpage is at https://premiertaco.github.io.

Overview

  • Premier-TACO introduces a novel framework designed for multitask offline visual representation pretraining in sequential decision-making tasks, utilizing a unique temporal action-driven contrastive learning objective.

  • By incorporating an innovative negative example sampling strategy, Premier-TACO reduces computational demands while ensuring focus on control-relevant information.

  • Empirical results from several continuous control benchmarks demonstrate Premier-TACO's effectiveness in few-shot imitation learning, outperforming existing baselines.

  • The research suggests potential future directions in enhancing AI and SDM tasks, including extending pretraining strategies and refining negative example sampling and contrastive loss functions.

Enhancing Few-Shot Policy Learning with Premier-TACO: A Multi-Task Offline Pretraining Approach

Introduction to Premier-TACO

Sequential decision-making (SDM) tasks are ubiquitous across various domains, from robotics to healthcare, presenting unique challenges for machine learning models due to their dynamic nature. Traditional pre-training methods that have succeeded in fields such as natural language processing and computer vision often fall short when directly applied to SDM tasks. Addressing this gap, we introduce Premier-TACO, a novel framework for multitask offline visual representation pretraining tailored for sequential decision-making problems. By advancing the temporal action-driven contrastive learning (TACO) objective with an efficient negative example sampling strategy, Premier-TACO paves the way for significant improvements in few-shot policy learning efficiency across a swath of continuous control benchmarks.

Premier-TACO's Innovations

The core innovation behind Premier-TACO lies in its temporal action-driven contrastive loss, designed to enhance the computation and performance efficiency of contrastive learning in the multitask setting. Key contributions include:

  • Novel Temporal Contrastive Learning Objective: Premier-TACO introduces a new temporal action-driven contrastive loss function, which facilitates learning a state representation by optimizing mutual information across state-action sequences. This enhances the model's ability to capture essential environmental dynamics for SDM tasks.
  • Efficient Negative Example Sampling: Unlike traditional approaches that consider every other data point as a negative example, Premier-TACO strategically samples a single, visually similar negative example from a proximate window. This not only reduces computational demands but also ensures the model focuses on control-relevant information.
  • Empirical Validation: Extensive empirical results across multiple continuous control benchmarks, such as the Deepmind Control Suite, MetaWorld, and LIBERO, underline Premier-TACO's superior ability to train robust visual representations. These results emphasize its significant outperformance in few-shot imitation learning of novel tasks over existing baselines.

Practical and Theoretical Implications

From a practical standpoint, Premier-TACO's ability to efficiently pretrain feature representations with high generalization capacity across tasks, embodiments, and observations indicates a major stride towards developing more adaptable and efficient AI models for SDM. Theoretically, this research provides valuable insights into the dynamics of multitask representation learning, particularly in leveraging temporal contrastive learning objectives to address the unique challenges of sequential decision-making tasks.

Future Developments in AI and Sequential Decision-Making

Premier-TACO's success suggests several avenues for future research, including exploring the extension of its pretraining strategy to other forms of sequential data beyond visual inputs. Additionally, investigating the integration of Premier-TACO with emerging models in other domains may yield new hybrid approaches with enhanced capabilities. As the field moves forward, further refinement of negative example sampling techniques and contrastive loss functions could unlock even greater efficiencies and performance gains in multitask offline pretraining and few-shot learning tasks.

In conclusion, Premier-TACO represents a significant advance in the pursuit of more adaptable and efficient AI models for sequential decision-making tasks. By addressing the specific needs of these challenges through a tailored pretraining approach, this research not only achieves state-of-the-art results across multiple benchmarks but also sets the stage for future innovations in the field.

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