PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations

• URL: http://arxiv.org/abs/2207.13224v1
• Date: Wed, 27 Jul 2022 00:26:15 GMT
• Title: PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations
• Authors: Kuang-Huei Lee, Ofir Nachum, Tingnan Zhang, Sergio Guadarrama, Jie Tan, Wenhao Yu
• Abstract summary: Evolution Strategy (ES) algorithms have shown promising results in training complex robotic control policies. PI-ARS combines a gradient-based representation learning technique, Predictive Information (PI), with a gradient-free ES algorithm, Augmented Random Search (ARS) We show PI-ARS demonstrates significantly better learning efficiency and performance compared to the ARS baseline.
• Score: 32.37414300338581
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evolution Strategy (ES) algorithms have shown promising results in training complex robotic control policies due to their massive parallelism capability, simple implementation, effective parameter-space exploration, and fast training time. However, a key limitation of ES is its scalability to large capacity models, including modern neural network architectures. In this work, we develop Predictive Information Augmented Random Search (PI-ARS) to mitigate this limitation by leveraging recent advancements in representation learning to reduce the parameter search space for ES. Namely, PI-ARS combines a gradient-based representation learning technique, Predictive Information (PI), with a gradient-free ES algorithm, Augmented Random Search (ARS), to train policies that can process complex robot sensory inputs and handle highly nonlinear robot dynamics. We evaluate PI-ARS on a set of challenging visual-locomotion tasks where a quadruped robot needs to walk on uneven stepping stones, quincuncial piles, and moving platforms, as well as to complete an indoor navigation task. Across all tasks, PI-ARS demonstrates significantly better learning efficiency and performance compared to the ARS baseline. We further validate our algorithm by demonstrating that the learned policies can successfully transfer to a real quadruped robot, for example, achieving a 100% success rate on the real-world stepping stone environment, dramatically improving prior results achieving 40% success.

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