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

IROS, pp. 3932-3939, 2017.

Cited by: 52|Views109


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



Get fulltext within 24h
Upload PDF

1.Your uploaded documents will be check within 24h, and coins will be credited to your account.

2.As the current system does not support cash withdrawal, you can add staff WeChat (AMxiaomai) to receive it as a red packet.

3.10 coins will be exchanged for 1 yuan.


Upload a single paper

for 5 coins

Wechat's Red Packet

Upload 50 articles

for 280 coins

Wechat's Red Packet

Upload 200 articles

for 1200 coins

Wechat's Red Packet

Upload 500 articles

for 3000 coins

Wechat's Red Packet

Upload 1000 articles

for 7000 coins

Wechat's Red Packet
Your rating :