Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification

• URL: http://arxiv.org/abs/2010.10644v4
• Date: Wed, 3 Mar 2021 09:54:49 GMT
• Title: Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification
• Authors: Daniel J. Mankowitz and Dan A. Calian and Rae Jeong and Cosmin Paduraru and Nicolas Heess and Sumanth Dathathri and Martin Riedmiller and Timothy Mann
• Abstract summary: Real-world systems are often subject to effects such as non-stationarity, wear-and-tear, uncalibrated sensors and so on. Such effects effectively perturb the system dynamics and can cause a policy trained successfully in one domain to perform poorly when deployed to a perturbed version of the same domain. This can affect a policy's ability to maximize future rewards as well as the extent to which it satisfies constraints. We present an algorithm that mitigates this form of misspecification, and showcase its performance in multiple simulated Mujoco tasks from the Real World Reinforcement Learning (RWRL)
• Score: 26.488582821511972
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many real-world physical control systems are required to satisfy constraints upon deployment. Furthermore, real-world systems are often subject to effects such as non-stationarity, wear-and-tear, uncalibrated sensors and so on. Such effects effectively perturb the system dynamics and can cause a policy trained successfully in one domain to perform poorly when deployed to a perturbed version of the same domain. This can affect a policy's ability to maximize future rewards as well as the extent to which it satisfies constraints. We refer to this as constrained model misspecification. We present an algorithm that mitigates this form of misspecification, and showcase its performance in multiple simulated Mujoco tasks from the Real World Reinforcement Learning (RWRL) suite.

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