  Past research has studied two approaches to utilise predefined policy sets in
repeated interactions: as experts, to dictate our own actions, and as types, to
characterise the behaviour of other agents. In this work, we bring these
complementary views together in the form of a novel meta-algorithm, called
Expert-HBA (E-HBA), which can be applied to any expert algorithm that considers
the average (or total) payoff an expert has yielded in the past. E-HBA
gradually mixes the past payoff with a predicted future payoff, which is
computed using the type-based characterisation. We present results from a
comprehensive set of repeated matrix games, comparing the performance of
several well-known expert algorithms with and without the aid of E-HBA. Our
results show that E-HBA has the potential to significantly improve the
performance of expert algorithms.
