2020/04/27 by Hao Sun, Xin-yu Pan, Sun, Hao +8
Computer Science · Engineering · Mathematics · #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reinforcement Learning in Robotics #Robot Manipulation and Learning #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2004.12909
openalex publication_date 2020/04/27 · arxiv created 2020/04/30 · arxiv updated 2020/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Solving the Goal-Conditioned Reward Sparse (GCRS) task is a challenging reinforcement learning problem due to the sparsity of reward signals. In this work, we propose a new formulation of GCRS tasks from the perspective of the drifted random walk on the state space, and design a novel method called Evolutionary Stochastic Policy Distillation (ESPD) to solve them based on the insight of reducing the First Hitting Time of the stochastic process. As a self-imitate approach, ESPD enables a target policy to learn from a series of its stochastic variants through the technique of policy distillation (PD). The learning mechanism of ESPD can be considered as an Evolution Strategy (ES) that applies perturbations upon policy directly on the action space, with a SELECT function to check the superiority of stochastic variants and then use PD to update the policy. The experiments based on the MuJoCo robotics control suite show the high learning efficiency of the proposed method.