Title: Partial Advantage Estimator for Proximal Policy Optimization. Abstract: Estimation of value in policy gradient methods is a fundamental problem. Generalized Advantage Estimation (GAE) is an exponentially-weighted estimator of an advantage function similar to TD($\lambda$). It substantially reduces the variance of policy gradient estimates at the expense of bias. In practical applications, a truncated GAE is used due to the incompleteness of the trajectory, which results in a large bias during estimation. To address this challenge, instead of using the all truncated GAE, we propose to take a part of the calculated GAE for updates, which significantly reduces the bias due to the incomplete trajectory.  We perform experiments in MuJoCo and $\mu$RTS to investigate the effect of different partial coefficient and sampling lengths. We show that our partial GAE approach yields better empirical results in both environments.