  Exploration and credit assignment under sparse rewards are still challenging
problems. We argue that these challenges arise in part due to the intrinsic
rigidity of operating at the level of actions. Actions can precisely define how
to perform an activity but are ill-suited to describe what activity to perform.
Instead, causal effects are inherently composable and temporally abstract,
making them ideal for descriptive tasks. By leveraging a hierarchy of causal
effects, this study aims to expedite the learning of task-specific behavior and
aid exploration. Borrowing counterfactual and normality measures from causal
literature, we disentangle controllable effects from effects caused by other
dynamics of the environment. We propose CEHRL, a hierarchical method that
models the distribution of controllable effects using a Variational
Autoencoder. This distribution is used by a high-level policy to 1) explore the
environment via random effect exploration so that novel effects are
continuously discovered and learned, and to 2) learn task-specific behavior by
prioritizing the effects that maximize a given reward function. In comparison
to exploring with random actions, experimental results show that random effect
exploration is a more efficient mechanism and that by assigning credit to few
effects rather than many actions, CEHRL learns tasks more rapidly.
