  Imitation Learning (IL) is a machine learning approach to learn a policy from
a dataset of demonstrations. IL can be useful to kick-start learning before
applying reinforcement learning (RL) but it can also be useful on its own, e.g.
to learn to imitate human players in video games. However, a major limitation
of current IL approaches is that they learn only a single "average" policy
based on a dataset that possibly contains demonstrations of numerous different
types of behaviors. In this paper, we propose a new approach called Behavioral
Repertoire Imitation Learning (BRIL) that instead learns a repertoire of
behaviors from a set of demonstrations by augmenting the state-action pairs
with behavioral descriptions. The outcome of this approach is a single neural
network policy conditioned on a behavior description that can be precisely
modulated. We apply this approach to train a policy on 7,777 human replays to
perform build-order planning in StarCraft II. Principal Component Analysis
(PCA) is applied to construct a low-dimensional behavioral space from the
high-dimensional army unit composition of each demonstration. The results
demonstrate that the learned policy can be effectively manipulated to express
distinct behaviors. Additionally, by applying the UCB1 algorithm, we are able
to adapt the behavior of the policy - in-between games - to reach a performance
beyond that of the traditional IL baseline approach.
