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

• URL: http://arxiv.org/abs/2008.06389v1
• Date: Fri, 14 Aug 2020 14:25:59 GMT
• Title: Sample-efficient Cross-Entropy Method for Real-time Planning
• Abstract summary: We propose an improved version of the Cross-Entropy Method (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.
• Score: 25.056882970766768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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