Transfer from Multiple Linear Predictive State Representations (PSR)
This work addresses policy transfer in partially observable domains, which is an incremental step as it extends transfer learning from fully observable to partially observable settings.
The paper tackles the problem of transferring policies from multiple partially observable source environments to a partially observable target environment modeled as predictive state representations, developing algorithms that successfully achieve transfer when models of both source and target tasks are available, though it discusses their performance and shortcomings without providing concrete numbers.
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.