  Shared feature spaces for actor-critic methods aims to capture generalized
latent representations to be used by the policy and value function with the
hopes for a more stable and sample-efficient optimization. However, such a
paradigm present a number of challenges in practice, as parameters generating a
shared representation must learn off two distinct objectives, resulting in
competing updates and learning perturbations. In this paper, we present a novel
feature-sharing framework to address these difficulties by introducing the mix
and mask mechanisms and the distributional scalarization technique. These
mechanisms behaves dynamically to couple and decouple connected latent features
variably between the policy and value function, while the distributional
scalarization standardizes the two objectives using a probabilistic standpoint.
From our experimental results, we demonstrate significant performance
improvements compared to alternative methods using separate networks and
networks with a shared backbone.
