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

Takumi Tanabe, Rei Sato, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto

Keywords: Deep Reinforcement Learning, Maxmin Optimization, Sim2Real

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.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/max-min-off-policy-actor-critic-method/code)