Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models

• URL: http://arxiv.org/abs/2307.08168v2
• Date: Mon, 6 Nov 2023 15:15:38 GMT
• Title: Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models
• Authors: Tyler Westenbroek, Jacob Levy, David Fridovich-Keil
• Abstract summary: We focus on developing efficient and reliable policy optimization strategies for robot learning with real-world data. In this paper we introduce a novel policy gradient-based policy optimization framework. We show that our approach can learn precise control strategies reliably and with only minutes of real-world data.
• Score: 10.472792899267365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We focus on developing efficient and reliable policy optimization strategies for robot learning with real-world data. In recent years, policy gradient methods have emerged as a promising paradigm for training control policies in simulation. However, these approaches often remain too data inefficient or unreliable to train on real robotic hardware. In this paper we introduce a novel policy gradient-based policy optimization framework which systematically leverages a (possibly highly simplified) first-principles model and enables learning precise control policies with limited amounts of real-world data. Our approach $1)$ uses the derivatives of the model to produce sample-efficient estimates of the policy gradient and $2)$ uses the model to design a low-level tracking controller, which is embedded in the policy class. Theoretical analysis provides insight into how the presence of this feedback controller overcomes key limitations of stand-alone policy gradient methods, while hardware experiments with a small car and quadruped demonstrate that our approach can learn precise control strategies reliably and with only minutes of real-world data.

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