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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.