  This paper proposes a novel deep reinforcement learning architecture that was
inspired by previous tree structured architectures which were only useable in
discrete action spaces. Policy Prediction Network offers a way to improve
sample complexity and performance on continuous control problems in exchange
for extra computation at training time but at no cost in computation at rollout
time. Our approach integrates a mix between model-free and model-based
reinforcement learning. Policy Prediction Network is the first to introduce
implicit model-based learning to Policy Gradient algorithms for continuous
action space and is made possible via the empirically justified clipping
scheme. Our experiments are focused on the MuJoCo environments so that they can
be compared with similar work done in this area.
