  Hierarchical reinforcement learning approaches learn policies based on
hierarchical decision structures. However, training such methods in practice
may lead to poor generalization, with either sub-policies executing actions for
too few time steps or devolving into a single policy altogether. In our work,
we introduce an alternative approach to sequentially learn such skills without
using an overarching hierarchical policy, in the context of environments in
which an objective of the agent is to prolong the episode for as long as
possible, or in other words `stay alive'. We demonstrate the utility of our
approach in a simulated 3D navigation environment which we have built. We show
that our method outperforms prior methods such as Soft Actor Critic and Soft
Option Critic on our environment, as well as the Atari River Raid environment.
