  This paper explores the problem of simultaneously learning a value function
and policy in deep actor-critic reinforcement learning models. We find that the
common practice of learning these functions jointly is sub-optimal, due to an
order-of-magnitude difference in noise levels between these two tasks. Instead,
we show that learning these tasks independently, but with a constrained
distillation phase, significantly improves performance. Furthermore, we find
that the policy gradient noise levels can be decreased by using a lower
\textit{variance} return estimate. Whereas, the value learning noise level
decreases with a lower \textit{bias} estimate. Together these insights inform
an extension to Proximal Policy Optimization we call \textit{Dual Network
Architecture} (DNA), which significantly outperforms its predecessor. DNA also
exceeds the performance of the popular Rainbow DQN algorithm on four of the
five environments tested, even under more difficult stochastic control
settings.
