Reinforcement Learning (RL) is notoriously data-inefficient, which makes
training on a real robot difficult. While model-based RL algorithms (world
models) improve data-efficiency to some extent, they still require hours or
days of interaction to learn skills. Recently, offline RL has been proposed as
a framework for training RL policies on pre-existing datasets without any
online interaction. However, constraining an algorithm to a fixed dataset
induces a state-action distribution shift between training and inference, and
limits its applicability to new tasks. In this work, we seek to get the best of
both worlds: we consider the problem of pretraining a world model with offline
data collected on a real robot, and then finetuning the model on online data
collected by planning with the learned model. To mitigate extrapolation errors
during online interaction, we propose to regularize the planner at test-time by
balancing estimated returns and (epistemic) model uncertainty. We evaluate our
method on a variety of visuo-motor control tasks in simulation and on a real
robot, and find that our method enables few-shot finetuning to seen and unseen
tasks even when offline data is limited. Videos, code, and data are available
at this https URL .

通过使用离线数据集在真实机器人上对世界模型进行预训练，然后通过使用学习模型进行在线数据集的规划和微调，本文试图解决强化学习在真实机器人上训练时的数据效率问题，以及模型在训练和推理过程中的分布偏移问题，该方法在模拟环境和真实机器人上的视觉 - 动作控制任务上进行了验证，发现即使离线数据有限，该方法也能实现对已知和未知任务的少次数微调。