Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model

Autor:	Yuan, Xiu, Mu, Tongzhou, Tao, Stone, Fang, Yunhao, Zhang, Mengke, Su, Hao
Rok vydání:	2024
Předmět:	Computer Science - Robotics Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Recent advancements in robot learning have used imitation learning with large models and extensive demonstrations to develop effective policies. However, these models are often limited by the quantity, quality, and diversity of demonstrations. This paper explores improving offline-trained imitation learning models through online interactions with the environment. We introduce Policy Decorator, which uses a model-agnostic residual policy to refine large imitation learning models during online interactions. By implementing controlled exploration strategies, Policy Decorator enables stable, sample-efficient online learning. Our evaluation spans eight tasks across two benchmarks-ManiSkill and Adroit-and involves two state-of-the-art imitation learning models (Behavior Transformer and Diffusion Policy). The results show Policy Decorator effectively improves the offline-trained policies and preserves the smooth motion of imitation learning models, avoiding the erratic behaviors of pure RL policies. See our project page (https://policydecorator.github.io) for videos. Comment: Explore videos, data, code, and more at https://policydecorator.github.io
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.13630 Zobrazit plný text záznamu View this record from Arxiv