Summary of Premier-taco Is a Few-shot Policy Learner: Pretraining Multitask Representation Via Temporal Action-driven Contrastive Loss, by Ruijie Zheng et al.
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
by Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Shuang Ma, Hal Daumé III, Huazhe Xu, John Langford, Praveen Palanisamy, Kalyan Shankar Basu, Furong Huang
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Premier-TACO approach improves few-shot policy learning efficiency in sequential decision-making tasks by leveraging multitask offline datasets for pretraining a general feature representation. This representation captures critical environmental dynamics and is fine-tuned using minimal expert demonstrations. By incorporating a novel negative example sampling strategy, Premier-TACO advances the temporal action contrastive learning (TACO) objective, which has achieved state-of-the-art results in visual control tasks. The approach significantly boosts TACO’s computational efficiency, making large-scale multitask offline pretraining feasible. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Premier-TACO is a new way to learn features that can be used for many different tasks. This helps with learning new skills quickly. It does this by using some data from other tasks and then fine-tuning it with very little help. The approach also makes sure the training process is efficient, which allows it to handle big datasets. Premier-TACO has been tested on many different control benchmarks and shows great results. |
Keywords
* Artificial intelligence * Few shot * Fine tuning * Pretraining
