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Policy gradient methods have achieved remarkable successes in solvingchallenging reinforcement learning problems. However, it still often suffersfrom the large variance issue on policy gradient estimation, which leads topoor sample efficiency during training. In this work, we propose a controlvariate method to effectively reduce variance for policy gradient methods.Motivated by the Stein's identity, our method extends the previous controlvariate methods used in REINFORCE and advantage actor-critic by introducingmore general action-dependent baseline functions. Empirical studies show thatour method significantly improves the sample efficiency of the state-of-the-artpolicy gradient approaches.

Action-depedent Control Variates for Policy Optimization via Stein's Identity