Papers › Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration

Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration

23 Oct 2024arXiv:2410.18076archive 2025-07-28

Max Wilcoxson, Qiyang Li, Kevin Frans, Sergey Levine

Unsupervised pretraining has been transformative in many supervised domains. However, applying such ideas to reinforcement learning (RL) presents a unique challenge in that fine-tuning does not involve mimicking task-specific data, but rather exploring and locating the solution through iterative self-improvement. In this work, we study how unlabeled prior trajectory data can be leveraged to learn efficient exploration strategies. While prior data can be used to pretrain a set of low-level skills, or as additional off-policy data for online RL, it has been unclear how to combine these ideas effectively for online exploration. Our method SUPE (Skills from Unlabeled Prior data for Exploration) demonstrates that a careful combination of these ideas compounds their benefits. Our method first extracts low-level skills using a variational autoencoder (VAE), and then pseudo-relabels unlabeled trajectories using an optimistic reward model, transforming prior data into high-level, task-relevant examples. Finally, SUPE uses these transformed examples as additional off-policy data for online RL to learn a high-level policy that composes pretrained low-level skills to explore efficiently. We empirically show that SUPE reliably outperforms prior strategies, successfully solving a suite of long-horizon, sparse-reward tasks. Code: https://github.com/rail-berkeley/supe.

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list_2d_of_dicts_to_dict_of_2d_lists rail-berkeley/supe/supe/utils.py official repository unverified MIT (permissive) · 662f22bdb29e3d83 · report
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subsample_ensemble rail-berkeley/supe/supe/networks/ensemble.py official repository unverified MIT (permissive) · 84f8f91dd99ce50f · report
update_target_network rail-berkeley/supe/supe/agents/model.py official repository unverified MIT (permissive) · f23c0c9faf00d830 · report

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Efficient ExplorationReinforcement Learning (RL)

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