论文标题
使用基于模型的元适应的机器人控制
Robotic Control Using Model Based Meta Adaption
论文作者
论文摘要
在机器学习中，元学习方法的目的是快速适应使用先验知识的未知任务。基于模型的元强化学习将通过世界模型与元强化学习（MRL）结合了增强学习，以提高样本效率。但是，对未知任务的适应并不总是会导致可取的代理行为。本文介绍了一种新的Meta适应控制器（MAC），该控制器（MAC）采用MRL将首选的机器人行为从一个任务应用于许多类似的任务。为此，MAC的目的是寻找代理商必须采取新任务以达到与学习任务相似的结果的行动。结果，代理人将迅速适应动态的变化并适当地表现，而无需构建执行首选行为的奖励函数。
In machine learning, meta-learning methods aim for fast adaptability to unknown tasks using prior knowledge. Model-based meta-reinforcement learning combines reinforcement learning via world models with Meta Reinforcement Learning (MRL) for increased sample efficiency. However, adaption to unknown tasks does not always result in preferable agent behavior. This paper introduces a new Meta Adaptation Controller (MAC) that employs MRL to apply a preferred robot behavior from one task to many similar tasks. To do this, MAC aims to find actions an agent has to take in a new task to reach a similar outcome as in a learned task. As a result, the agent will adapt quickly to the change in the dynamic and behave appropriately without the need to construct a reward function that enforces the preferred behavior.
