Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification

• URL: http://arxiv.org/abs/2211.03413v1
• Date: Mon, 7 Nov 2022 10:18:31 GMT
• Title: Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification
• Authors: Takumi Tanabe, Rei Sato, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
• Abstract summary: This study focuses on scenarios involving a simulation environment with uncertainty parameters and the set of their possible values. The aim is to optimize the worst-case performance on the uncertainty parameter set to guarantee the performance in the corresponding real-world environment. Experiments in multi-joint dynamics with contact (MuJoCo) environments show that the proposed method exhibited a worst-case performance superior to several baseline approaches.
• Score: 22.241676350331968
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the field of reinforcement learning, because of the high cost and risk of policy training in the real world, policies are trained in a simulation environment and transferred to the corresponding real-world environment. However, the simulation environment does not perfectly mimic the real-world environment, lead to model misspecification. Multiple studies report significant deterioration of policy performance in a real-world environment. In this study, we focus on scenarios involving a simulation environment with uncertainty parameters and the set of their possible values, called the uncertainty parameter set. The aim is to optimize the worst-case performance on the uncertainty parameter set to guarantee the performance in the corresponding real-world environment. To obtain a policy for the optimization, we propose an off-policy actor-critic approach called the Max-Min Twin Delayed Deep Deterministic Policy Gradient algorithm (M2TD3), which solves a max-min optimization problem using a simultaneous gradient ascent descent approach. Experiments in multi-joint dynamics with contact (MuJoCo) environments show that the proposed method exhibited a worst-case performance superior to several baseline approaches.

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