Sample-efficient Cross-Entropy Method for Real-time Planning

2020/08/14 by Cristina Pinneri, Shambhuraj Sawant, Pinneri, Cristina +11 · 13 citations
Engineering · Computer Science · #Advanced Control Systems Optimization #Robotic Path Planning Algorithms #Reinforcement Learning in Robotics

paper · pdf · doi:10.48550/arxiv.2008.06389

Abstract

Trajectory optimizers for model-based reinforcement learning, such as the Cross-Entropy Method (CEM), can yield compelling results even in high-dimensional control tasks and sparse-reward environments. However, their sampling inefficiency prevents them from being used for real-time planning and control. We propose an improved version of the CEM algorithm for fast planning, with novel additions including temporally-correlated actions and memory, requiring 2.7-22x less samples and yielding a performance increase of 1.2-10x in high-dimensional control problems.

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