  The Sampled Policy Gradient (SPG) algorithm is a new offline actor-critic
variant that samples in the action space to approximate the policy gradient. It
does so by using the critic to evaluate the sampled actions. SPG offers
theoretical promise over similar algorithms such as DPG as it searches the
action-Q-value space independently of the local gradient, enabling it to avoid
local minima. This paper aims to compare SPG to two similar actor-critic
algorithms, CACLA and DPG. The comparison is made across two different
environments, two different network architectures, as well as training on
on-policy transitions in contrast to using an experience buffer. Results seem
to show that although SPG does often not perform the worst, it doesn't always
match the performance of the best performing algorithm at a particular task.
Further experiments are required to get a better estimate of the qualities of
SPG.
