Meta reinforcement learning with latent variable Gaussian processes

Learning from small data sets is critical in many practical applications where data col- lection is time consuming or expensive, e.g., robotics, animal experiments or drug design. Meta learning is one way to increase the data efficiency of learning algorithms by general- izing learned concepts from a set of training tasks to unseen, but related, tasks. Often, this relationship between tasks is hard coded or re- lies in some other way on human expertise. In this paper, we frame meta learning as a hi- erarchical latent variable model and infer the relationship between tasks automatically from data. We apply our framework in a model- based reinforcement learning setting and show that our meta-learning model effectively gen- eralizes to novel tasks by identifying how new tasks relate to prior ones from minimal data. This results in up to a 60% reduction in the average interaction time needed to solve tasks compared to strong baselines