  This work adopts the very successful distributional perspective on
reinforcement learning and adapts it to the continuous control setting. We
combine this within a distributed framework for off-policy learning in order to
develop what we call the Distributed Distributional Deep Deterministic Policy
Gradient algorithm, D4PG. We also combine this technique with a number of
additional, simple improvements such as the use of $N$-step returns and
prioritized experience replay. Experimentally we examine the contribution of
each of these individual components, and show how they interact, as well as
their combined contributions. Our results show that across a wide variety of
simple control tasks, difficult manipulation tasks, and a set of hard
obstacle-based locomotion tasks the D4PG algorithm achieves state of the art
performance.
