参考项目地址 - tedrepo/SimDial GitHub Wiki
https://github.com/anitan0925/vaegan
https://github.com/JeremyCCHsu/tf-vaegan
https://github.com/ceteke/tf_vaegan
https://github.com/tedrepo/vaegan
https://github.com/Gilgahex/VAEGAN
https://github.com/YazhouZhang0709/VAEGAN
https://github.com/YazhouZhang0709/VAEGAN
https://github.com/yuyingyeh/tensorflow-implementation-of-VAE-GAN-VAEGAN
基于变分自编码器的生成对抗网络（VEGAN） https://blog.csdn.net/g8015108/article/details/78072439
基于自编码器的生成对抗网络AEGAN https://blog.csdn.net/g8015108/article/details/78041154
对抗自编码 https://blog.csdn.net/touch_dream/article/details/77892215?readlog
https://blog.csdn.net/weixin_41036461/article/details/79802259
http://budzianowski.github.io/
https://github.com/slinderman/pyhsmm_spiketrains
https://github.com/jvkersch/hsmmlearn
https://github.com/albietz/online_hmm
https://github.com/yandexdataschool/nlp_course
https://github.com/yizt/numpy_neural_network
https://github.com/zxsted/Entropy-Regularized-RL 基于好奇心的 rl
https://github.com/tedrepo/deep_rl_maml
life long RL Policy and Value Transfer for Lifelong RL https://github.com/david-abel/transfer_rl_icml_2018
rl_abstraction https://github.com/david-abel/rl_abstraction
State Abstractions for Lifelong Reinforcement Learning https://david-abel.github.io/papers/lifelong_sa_icml_18.pdf
maml
https://coladrill.github.io/2018/10/24/%E5%85%83%E5%AD%A6%E4%B9%A0%E7%BB%BC%E8%BF%B0/
https://blog.csdn.net/qq_26564783/article/details/81706497
ICML2018】63篇强化学习论文全解读 https://blog.csdn.net/dQCFKyQDXYm3F8rB0/article/details/81230588
Meta-learning（元学习）和 3D-CNN 总结 https://blog.csdn.net/xzw95/article/details/81783046
强化学习中如何高效地与环境互动？如何从经验中高效学习？ http://www.dadejin.com/rr/d/805368.html
超越 DQN 和 A3C：深度強化學習領域近期新進展概覽 https://hk.saowen.com/a/716d323523df0472fdac2d3b94b70c841328c3961d8224a7d6e2f845ef291cb8
python 读取视频,处理后,实时计算帧数fps的方法 https://www.jb51.net/article/143543.htm
MDN
深度学习生成舞蹈影片01之MDN https://cloud.tencent.com/developer/article/1351760
http://cbonnett.github.io/MDN.html
Mixture Density Networks with TensorFlow http://blog.otoro.net/2015/11/24/mixture-density-networks-with-tensorflow/
学界 | DeepMind新论文提出循环环境模拟器：可适应多种不同环境 http://www.sohu.com/a/134761821_465975
https://github.com/openai/mlsh Meta-Learning Shared Hierarchies
car racing
https://github.com/PDillis/Experiment-CarRacing
https://github.com/hchkaiban/CarRacingRL_DDQN
https://github.com/hchkaiban/CarRacingImitationLearning
https://github.com/PDillis/DQN-CarRacing
https://github.com/PDillis/DQN-CarRacing/blob/master/DQN%20for%20CarRacing.ipynb
https://github.com/CaioCamatta/CarRacing-PolicyGradient
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on. https://github.com/Hironsan/anago
https://github.com/felipessalvatore/self_driving_pi_car
https://github.com/yizt/numpy_neural_network