Adversarially Robust Policy Learning: Active construction of physically-plausible perturbations

IROS, pp. 3932-3939, 2017.

Cited by: 52|Views109
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Abstract:

Policy search methods in reinforcement learning have demonstrated success in scaling up to larger problems beyond toy examples. However, deploying these methods on real robots remains challenging due to the large sample complexity required during learning and their vulnerability to malicious intervention. We introduce Adversarially Robust...More

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