Robust Reinforcement Learning under model misspecification

Abstract

Reinforcement learning has achieved remarkable performance in a wide range of tasks these days. Nevertheless, some unsolved problems limit its applications in real-world control. One of them is model misspecification, a situation where an agent is trained and deployed in environments with different transition dynamics. We propose an novel framework that utilize history trajectory and Partial Observable Markov Decision Process Modeling to deal with this dilemma. Additionally, we put forward an efficient adversarial attack method to assist robust training. Our experiments in four gym domains validate the effectiveness of our framework.

References in corpus (5)

• Challenges of Real-World Reinforcement Learning
• Delving into adversarial attacks on deep policies
• Adversary A3C for Robust Reinforcement Learning
• Distributionally Robust Reinforcement Learning
• Risk Averse Robust Adversarial Reinforcement Learning