  This paper deals with the problem of learning a skill-conditioned policy that
acts meaningfully in the absence of a reward signal. Mutual information based
objectives have shown some success in learning skills that reach a diverse set
of states in this setting. These objectives include a KL-divergence term, which
is maximized by visiting distinct states even if those states are not far apart
in the MDP. This paper presents an approach that rewards the agent for learning
skills that maximize the Wasserstein distance of their state visitation from
the start state of the skill. It shows that such an objective leads to a policy
that covers more distance in the MDP than diversity based objectives, and
validates the results on a variety of Atari environments.
