  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.
