Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
<|reference_start|>Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss: 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.<|reference_end|>
@article{zheng2024premier-taco,
    title={Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask
  Representation via Temporal Action-Driven Contrastive Loss},
    author={Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Shuang Ma, Hal Daum'e III,
  Huazhe Xu, John Langford, Praveen Palanisamy, Kalyan Shankar Basu, Furong
  Huang},
    journal={arXiv preprint arXiv:2402.06187},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06187},
    primaryClass={cs.LG cs.AI cs.RO}
}