FOSP: Fine-tuning Offline Safe Policy through World Models

• URL: http://arxiv.org/abs/2407.04942v1
• Date: Sat, 6 Jul 2024 03:22:57 GMT
• Title: FOSP: Fine-tuning Offline Safe Policy through World Models
• Authors: Chenyang Cao, Yucheng Xin, Silang Wu, Longxiang He, Zichen Yan, Junbo Tan, Xueqian Wang,
• Abstract summary: Model-based Reinforcement Learning (RL) has shown its high training efficiency and capability of handling high-dimensional tasks. However, prior works still pose safety challenges due to the online exploration in real-world deployment. In this paper, we aim to further enhance safety during the deployment stage for vision-based robotic tasks by fine-tuning an offline-trained policy.
• Score: 3.7971075341023526
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model-based Reinforcement Learning (RL) has shown its high training efficiency and capability of handling high-dimensional tasks. Regarding safety issues, safe model-based RL can achieve nearly zero-cost performance and effectively manage the trade-off between performance and safety. Nevertheless, prior works still pose safety challenges due to the online exploration in real-world deployment. To address this, some offline RL methods have emerged as solutions, which learn from a static dataset in a safe way by avoiding interactions with the environment. In this paper, we aim to further enhance safety during the deployment stage for vision-based robotic tasks by fine-tuning an offline-trained policy. We incorporate in-sample optimization, model-based policy expansion, and reachability guidance to construct a safe offline-to-online framework. Moreover, our method proves to improve the generalization of offline policy in unseen safety-constrained scenarios. Finally, the efficiency of our method is validated on simulation benchmarks with five vision-only tasks and a real robot by solving some deployment problems using limited data.

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