Adaptive Adversarial Training for Meta Reinforcement Learning
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 29 references that are in the map. First posted on arXiv 27 Apr 2021.
Builds on
21 of 29 references.
References · 21
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
- 2015Trust Region Policy Optimizationcited by 2,503
- 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 542
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- 2017Adversarial Attacks on Neural Network Policiescited by 314
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- 2017Tactics of Adversarial Attack on Deep Reinforcement Learning Agentscited by 163
- 2017Robust Deep Reinforcement Learning with Adversarial Attackscited by 152
- 2017Delving into adversarial attacks on deep policiescited by 104
9 more
- 2018ProMP: Proximal Meta-Policy Searchcited by 91
- 2020Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learningcited by 81
- 2013Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizationscited by 68
- 2018Meta Reinforcement Learning with Latent Variable Gaussian Processescited by 44
- 2020Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabelingcited by 24
- 2018Adversarial Meta-Learningcited by 16
- 2019Adversarially Robust Few-Shot Learning: A Meta-Learning Approachcited by 6
- 2020Multimodal Aggregation Approach for Memory Vision-Voice Indoor Navigation with Meta-Learningcited by 6
- 2019Meta Dropout: Learning to Perturb Features for Generalizationcited by 3
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