  Deep Reinforcement Learning (DRL) algorithms for continuous action spaces are
known to be brittle toward hyperparameters as well as \cut{being}sample
inefficient. Soft Actor Critic (SAC) proposes an off-policy deep actor critic
algorithm within the maximum entropy RL framework which offers greater
stability and empirical gains. The choice of policy distribution, a factored
Gaussian, is motivated by \cut{chosen due}its easy re-parametrization rather
than its modeling power. We introduce Normalizing Flow policies within the SAC
framework that learn more expressive classes of policies than simple factored
Gaussians. \cut{We also present a series of stabilization tricks that enable
effective training of these policies in the RL setting.}We show empirically on
continuous grid world tasks that our approach increases stability and is better
suited to difficult exploration in sparse reward settings.
