Unsupervised-to-Online Reinforcement Learning

• URL: http://arxiv.org/abs/2408.14785v1
• Date: Tue, 27 Aug 2024 05:23:45 GMT
• Title: Unsupervised-to-Online Reinforcement Learning
• Authors: Junsu Kim, Seohong Park, Sergey Levine,
• Abstract summary: Unsupervised-to-online RL (U2O RL) replaces domain-specific supervised offline RL with unsupervised offline RL. U2O RL not only enables reusing a single pre-trained model for multiple downstream tasks, but also learns better representations. We empirically demonstrate that U2O RL achieves strong performance that matches or even outperforms previous offline-to-online RL approaches.
• Score: 59.910638327123394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Offline-to-online reinforcement learning (RL), a framework that trains a policy with offline RL and then further fine-tunes it with online RL, has been considered a promising recipe for data-driven decision-making. While sensible, this framework has drawbacks: it requires domain-specific offline RL pre-training for each task, and is often brittle in practice. In this work, we propose unsupervised-to-online RL (U2O RL), which replaces domain-specific supervised offline RL with unsupervised offline RL, as a better alternative to offline-to-online RL. U2O RL not only enables reusing a single pre-trained model for multiple downstream tasks, but also learns better representations, which often result in even better performance and stability than supervised offline-to-online RL. To instantiate U2O RL in practice, we propose a general recipe for U2O RL to bridge task-agnostic unsupervised offline skill-based policy pre-training and supervised online fine-tuning. Throughout our experiments in nine state-based and pixel-based environments, we empirically demonstrate that U2O RL achieves strong performance that matches or even outperforms previous offline-to-online RL approaches, while being able to reuse a single pre-trained model for a number of different downstream tasks.

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