  In this paper, we tackle the problem of transferring policy from multiple
partially observable source environments to a partially observable target
environment modeled as predictive state representation. This is an entirely new
approach with no previous work, other than the case of transfer in fully
observable domains. We develop algorithms to successfully achieve policy
transfer when we have the model of both the source and target tasks and discuss
in detail their performance and shortcomings. These algorithms could be a
starting point for the field of transfer learning in partial observability.
