$\texttt{PREMIER-TACO}$ is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
Keywords: Few-shot Imitation Learning, Temporal Action-Driven Contrastive learning, Premier-TACO
TL;DR: We introduce Premier-TACO, a novel multitask feature representation learning methodology aiming to enhance the efficiency of few-shot policy learning in sequential decision-making tasks.
Abstract: 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.
Submission Number: 55
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