  We present a novel method for learning a set of disentangled reward functions
that sum to the original environment reward and are constrained to be
independently obtainable. We define independent obtainability in terms of value
functions with respect to obtaining one learned reward while pursuing another
learned reward. Empirically, we illustrate that our method can learn meaningful
reward decompositions in a variety of domains and that these decompositions
exhibit some form of generalization performance when the environment's reward
is modified. Theoretically, we derive results about the effect of maximizing
our method's objective on the resulting reward functions and their
corresponding optimal policies.
