most citedMultiple Domain Cyberspace Attack and Defense Game Based on Reward Randomization Reinforcement Learning

4 citations · 4 across the 1 of their papers we have counts for

collaborators

2 papers

cs.AI2022★ 4 cited

Multiple Domain Cyberspace Attack and Defense Game Based on Reward Randomization Reinforcement Learning

Lei Zhang, Yu Pan, Yi Liu +2

The existing network attack and defense method can be regarded as game, but most of the game only involves network domain, not multiple domain cyberspace. To address this challenge…

cs.LG2019

Learning Continually from Low-shot Data Stream

Canyu Le, Xihan Wei, Biao Wang +2

While deep learning has achieved remarkable results on various applications, it is usually data hungry and struggles to learn over non-stationary data stream. To solve these two li…