Robotic Control Using Model Based Meta Adaption

In machine learning, meta-learning methods aim for fast adaptability to unknown tasks using prior knowledge. Model-based meta-reinforcement learning com- bines reinforcement learning via world models with Meta Re- inforcement 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.