  In this paper, we propose the first fully push-forward-based Distributional
Reinforcement Learning algorithm, called Push-forward-based Actor-Critic
EncourageR (PACER). Specifically, PACER establishes a stochastic utility value
policy gradient theorem and simultaneously leverages the push-forward operator
in the construction of both the actor and the critic. Moreover, based on
maximum mean discrepancies (MMD), a novel sample-based encourager is designed
to incentivize exploration. Experimental evaluations on various continuous
control benchmarks demonstrate the superiority of our algorithm over the
state-of-the-art.
