  We consider constrained policy optimization in Reinforcement Learning, where
the constraints are in form of marginals on state visitations and global action
executions. Given these distributions, we formulate policy optimization as
unbalanced optimal transport over the space of occupancy measures. We propose a
general purpose RL objective based on Bregman divergence and optimize it using
Dykstra's algorithm. The approach admits an actor-critic algorithm for when the
state or action space is large, and only samples from the marginals are
available. We discuss applications of our approach and provide demonstrations
to show the effectiveness of our algorithm.
