  We propose a framework for transferring any existing policy from a
potentially unknown source MDP to a target MDP. This framework (1) enables
reuse in the target domain of any form of source policy, including classical
controllers, heuristic policies, or deep neural network-based policies, (2)
attains optimality under suitable theoretical conditions, and (3) guarantees
improvement over the source policy in the target MDP. These are achieved by
packaging the source policy as a black-box option in the target MDP and
providing a theoretically grounded way to learn the option's initiation set
through general value functions. Our approach facilitates the learning of new
policies by (1) maximizing the target MDP reward with the help of the black-box
option, and (2) returning the agent to states in the learned initiation set of
the black-box option where it is already optimal. We show that these two
variants are equivalent in performance under some conditions. Through a series
of experiments in simulated environments, we demonstrate that our framework
performs excellently in sparse reward problems given (sub-)optimal source
policies and improves upon prior art in transfer methods such as continual
learning and progressive networks, which lack our framework's desirable
theoretical properties.
