  Reinforcement learning algorithms are typically limited to learning a single
solution for a specified task, even though diverse solutions often exist.
Recent studies showed that learning a set of diverse solutions is beneficial
because diversity enables robust few-shot adaptation. Although existing methods
learn diverse solutions by using the mutual information as unsupervised
rewards, such an approach often suffers from the bias of the gradient estimator
induced by value function approximation. In this study, we propose a novel
method that can learn diverse solutions without suffering the bias problem. In
our method, a policy conditioned on a continuous or discrete latent variable is
trained by directly maximizing the variational lower bound of the mutual
information, instead of using the mutual information as unsupervised rewards as
in previous studies. Through extensive experiments on robot locomotion tasks,
we demonstrate that the proposed method successfully learns an infinite set of
diverse solutions by learning continuous latent variables, which is more
challenging than learning a finite number of solutions. Subsequently, we show
that our method enables more effective few-shot adaptation compared with
existing methods.
