  In this paper, we propose a new algorithm PPG (Proximal Policy Gradient),
which is close to both VPG (vanilla policy gradient) and PPO (proximal policy
optimization). The PPG objective is a partial variation of the VPG objective
and the gradient of the PPG objective is exactly same as the gradient of the
VPG objective. To increase the number of policy update iterations, we introduce
the advantage-policy plane and design a new clipping strategy. We perform
experiments in OpenAI Gym and Bullet robotics environments for ten random
seeds. The performance of PPG is comparable to PPO, and the entropy decays
slower than PPG. Thus we show that performance similar to PPO can be obtained
by using the gradient formula from the original policy gradient theorem.
