  This paper presents a deep Inverse Reinforcement Learning (IRL) framework
that can learn an a priori unknown number of nonlinear reward functions from
unlabeled experts' demonstrations. For this purpose, we employ the tools from
Dirichlet processes and propose an adaptive approach to simultaneously account
for both complex and unknown number of reward functions. Using the conditional
maximum entropy principle, we model the experts' multi-intention behaviors as a
mixture of latent intention distributions and derive two algorithms to estimate
the parameters of the deep reward network along with the number of experts'
intentions from unlabeled demonstrations. The proposed algorithms are evaluated
on three benchmarks, two of which have been specifically extended in this study
for multi-intention IRL, and compared with well-known baselines. We demonstrate
through several experiments the advantages of our algorithms over the existing
approaches and the benefits of online inferring, rather than fixing beforehand,
the number of expert's intentions.
