@inproceedings{
zheng2023textttpremiertaco,
title={\${\textbackslash}texttt\{{PREMIER}-{TACO}\}\$ is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss},
author={Ruijie Zheng and Yongyuan Liang and Xiyao Wang and Shuang Ma and Hal Daum{\'e} III and Huazhe Xu and John Langford and Praveen Palanisamy and Kalyan Basu and Furong Huang},
booktitle={NeurIPS 2023 Foundation Models for Decision Making Workshop},
year={2023},
url={https://openreview.net/forum?id=XT1phTGH76}
}
We introduce $\texttt{Premier-TACO}$, a novel multitask feature representation learning methodology aiming to enhance the efficiency of few-shot policy learning in sequential decision-making tasks. $\texttt{Premier-TACO}$ pretrains a general feature representation using a small subset of relevant multitask offline datasets, capturing essential environmental dynamics. This representation can then be fine-tuned to specific tasks with few expert demonstrations. Building upon the recent temporal action contrastive learning (TACO) objective, which obtains the state of art performance in visual control tasks, $\texttt{Premier-TACO}$ additionally employs a simple yet effective negative example sampling strategy. This key modification ensures computational efficiency and scalability for large-scale multitask offline pretraining. Experimental results from both Deepmind Control Suite and MetaWorld domains underscore the effectiveness of $\texttt{Premier-TACO}$ for pretraining visual representation, facilitating efficient few-shot imitation learning of unseen tasks. On the DeepMind Control Suite, $\texttt{Premier-TACO}$ achieves an average improvement of 101% in comparison to a carefully implemented Learn-from-scratch baseline, and a 24% improvement compared with the most effective baseline pretraining method. Similarly, on MetaWorld, $\texttt{Premier-TACO}$ obtains an average advancement of 74% against Learn-from-scratch and a 40% increase in comparison to the best baseline pretraining method.
 is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
Ruijie Zheng Yongyuan Liang Xiyao Wang Shuang Ma Hal Daumé III Huazhe Xu John Langford Praveen Palanisamy Kalyan Basu Furong Huang