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Adaptive Adversarial Training for Meta Reinforcement Learning

2021-04-27 16:23:34
Shiqi Chen, Zhengyu Chen, Donglin Wang
arXiv_AI
           
Abstract

Meta Reinforcement Learning (MRL) enables an agent to learn from a limited number of past trajectories and extrapolate to a new task. In this paper, we attempt to improve the robustness of MRL. We build upon model-agnostic meta-learning (MAML) and propose a novel method to generate adversarial samples for MRL by using Generative Adversarial Network (GAN). That allows us to enhance the robustness of MRL to adversal attacks by leveraging these attacks during meta training process.

Abstract (translated)

URL

https://arxiv.org/abs/2104.13302

PDF

https://arxiv.org/pdf/2104.13302.pdf

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