  Continuous control tasks in reinforcement learning are important because they
provide an important framework for learning in high-dimensional state spaces
with deceptive rewards, where the agent can easily become trapped into
suboptimal solutions. One way to avoid local optima is to use a population of
agents to ensure coverage of the policy space, yet learning a population with
the "best" coverage is still an open problem. In this work, we present a novel
approach to population-based RL in continuous control that leverages properties
of normalizing flows to perform attractive and repulsive operations between
current members of the population and previously observed policies. Empirical
results on the MuJoCo suite demonstrate a high performance gain for our
algorithm compared to prior work, including Soft-Actor Critic (SAC).
