Accelerating Reinforcement Learning with a Directional-Gaussian-Smoothing Evolution Strategy

• URL: http://arxiv.org/abs/2002.09077v1
• Date: Fri, 21 Feb 2020 01:05:57 GMT
• Title: Accelerating Reinforcement Learning with a Directional-Gaussian-Smoothing Evolution Strategy
• Authors: Jiaxing Zhang, Hoang Tran, Guannan Zhang
• Abstract summary: Evolution strategy (ES) has been shown great promise in many challenging reinforcement learning (RL) tasks. There are two limitations in the current ES practice that may hinder its otherwise further capabilities. In this work, we employ a Directional Gaussian Smoothing Evolutionary Strategy (DGS-ES) to accelerate RL training. We show that DGS-ES is highly scalable, possesses superior wall-clock time, and achieves competitive reward scores to other popular policy gradient and ES approaches.
• Score: 3.404507240556492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evolution strategy (ES) has been shown great promise in many challenging reinforcement learning (RL) tasks, rivaling other state-of-the-art deep RL methods. Yet, there are two limitations in the current ES practice that may hinder its otherwise further capabilities. First, most current methods rely on Monte Carlo type gradient estimators to suggest search direction, where the policy parameter is, in general, randomly sampled. Due to the low accuracy of such estimators, the RL training may suffer from slow convergence and require more iterations to reach optimal solution. Secondly, the landscape of reward functions can be deceptive and contains many local maxima, causing ES algorithms to prematurely converge and be unable to explore other parts of the parameter space with potentially greater rewards. In this work, we employ a Directional Gaussian Smoothing Evolutionary Strategy (DGS-ES) to accelerate RL training, which is well-suited to address these two challenges with its ability to i) provide gradient estimates with high accuracy, and ii) find nonlocal search direction which lays stress on large-scale variation of the reward function and disregards local fluctuation. Through several benchmark RL tasks demonstrated herein, we show that DGS-ES is highly scalable, possesses superior wall-clock time, and achieves competitive reward scores to other popular policy gradient and ES approaches.

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