update readme

README.md

@@ -16,13 +16,12 @@ Tutorials are not finished. See some [examples](examples) to learn about the fra
 + [Similarity Learning on MNIST](examples/SimilarityLearning)
 
 ### Reinforcement Learning:
-+ [Deep Q-Network(DQN) variants on Atari games](examples/DeepQNetwork)
++ [Deep Q-Network(DQN) variants on Atari games](examples/DeepQNetwork), including DQN, DoubleDQN, DuelingDQN.
 + [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](examples/A3C-Gym)
 
 ### Unsupervised Learning:
 + [Generative Adversarial Network(GAN) variants](examples/GAN), including DCGAN, InfoGAN, Conditional GAN, WGAN, Image to Image.
 
-
 ### Speech / NLP:
 + [LSTM-CTC for speech recognition](examples/CTC-TIMIT)
 + [char-rnn for fun](examples/Char-RNN)

examples/README.md

@@ -18,7 +18,7 @@ Training examples with __reproducible__ and meaningful performance.
 + Load a pre-trained [Convolutional Pose Machines](ConvolutionalPoseMachines/).
 
 ## Reinforcement Learning:
-+ [Deep Q-Network(DQN) variants on Atari games](DeepQNetwork)
++ [Deep Q-Network(DQN) variants on Atari games](DeepQNetwork), including DQN, DoubleDQN, DuelingDQN.
 + [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](A3C-Gym)
 
 ## Unsupervised Learning:

tensorpack/README.md

-
-## Tensorpack High-Level Tutorial
-
-### How to feed data
-Define a `DataFlow` instance to feed data.
-See [Dataflow documentation](https://github.com/ppwwyyxx/tensorpack/tree/master/tensorpack/dataflow)
-
-### How to define a model
-
-Take a look at [mnist example](https://github.com/ppwwyyxx/tensorpack/blob/master/example_mnist.py) first.
-
-### How to perform training
-
-
-### How to perform inference
-
-
-### How to add new models
-
-### Use tensorboard summary