关于Noisy Networks for Exploration dqn的原始论文，适合初学者对深度强化学习Noisy Networks for Exploration dqn的认识和了解Published as a conference paper at ICLR 2018
T is assessed by the action-value function Q defined as
Q"(.a)=配
∑
rR(t, at)
(1)
where E is the expectation ove
关于duelingdqn的原始论文，适合初学者对深度强化学习duelingdqn的认识和了解Dueling Network Architectures for Deep Reinforcement Learning
et al.(2016). The results of Schaul et al.(2016) are the 2.1. Deep Q-networks
current published state-of-the-art
The value functions as descri
本课件讲解了强化学习的基本问题，经典Q学习理论，深度Q学习理论和程序讲解与训练。强化学习相关参考资料
网络资源
01
https://www.intelnervana.com/demystifying-deep-reinforcement-learning/
http://artint.info/html/artint265.html
参考文献
02
Playing Atari with Deep Reinforcement Learning 2013: arXiv: 1312.5602v1
C
Deep Reinforcement Learning Hands-On
by Maxim LapanTable of contents
Deep reinforcement Learning Hands-On
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基于无双线性对的无证书隐式认证的Kerberos协议改进，：针对Ｋｅｒｂｅｒｏｓ认证协议存在的密钥托管，口令攻击和重放攻击等缺陷，将隐式认证与无证
书密钥协商协议结合，提出了一种无双线性对的无证书隐式认证的Ｋｅｒｂｅｒｏｓ改进协议，避免了Ｋｅｒｂｅｒｏｓ
协议中第三方对信息的无举证窃听，有效克服了中间人攻击。新协议在增强模型下是可证明安全的，并
且仅需９次椭圆曲线上的点乘运算和２次哈希运算，具有较高的计算效率。27
Kerb
XA,XB,TA,T8,Ka1,K,K丽,KB,KB5)。
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