title: Policy Optimization with Stochastic Mirror Descent
abstract: Improving sample efficiency has been a longstanding goal in reinforcement learning.
  In this paper, we propose the $\mathtt{VRMPO}$: a sample efficient policy gradient method with stochastic mirror descent.
  A novel variance reduced policy gradient estimator is the key of $\mathtt{VRMPO}$ to improve sample efficiency.
  Our $\mathtt{VRMPO}$ needs only $\mathcal{O}(\epsilon^{-3})$ sample trajectories to achieve an $\epsilon$-approximate first-order stationary point, 
  which matches the best-known sample complexity.
  We conduct extensive experiments to show our algorithm outperforms state-of-the-art policy gradient methods in various settings.
authors: Long Yang Gang Zheng Zavier Zhang Yu Zhang Qian Zheng Jun Wen Gang Pana sample efficient policy gradient method with stochastic mirror descent.
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