  The idea of reusing information from previously learned tasks (source tasks)
for the learning of new tasks (target tasks) has the potential to significantly
improve the sample efficiency reinforcement learning agents. In this work, we
describe an approach to concisely store and represent learned task knowledge,
and reuse it by allowing it to guide the exploration of an agent while it
learns new tasks. In order to do so, we use a measure of similarity that is
defined directly in the space of parameterized representations of the value
functions. This similarity measure is also used as a basis for a variant of the
growing self-organizing map algorithm, which is simultaneously used to enable
the storage of previously acquired task knowledge in an adaptive and scalable
manner.We empirically validate our approach in a simulated navigation
environment and discuss possible extensions to this approach along with
potential applications where it could be particularly useful.
