  Approaches to policy optimization have been motivated from diverse
principles, based on how the parametric model is interpreted (e.g. value versus
policy representation) or how the learning objective is formulated, yet they
share a common goal of maximizing expected return. To better capture the
commonalities and identify key differences between policy optimization methods,
we develop a unified perspective that re-expresses the underlying updates in
terms of a limited choice of gradient form and scaling function. In particular,
we identify a parameterized space of approximate gradient updates for policy
optimization that is highly structured, yet covers both classical and recent
examples, including PPO. As a result, we obtain novel yet well motivated
updates that generalize existing algorithms in a way that can deliver benefits
both in terms of convergence speed and final result quality. An experimental
investigation demonstrates that the additional degrees of freedom provided in
the parameterized family of updates can be leveraged to obtain non-trivial
improvements both in synthetic domains and on popular deep RL benchmarks.
