rename some examples

README.md

@@ -13,15 +13,15 @@ Docs & tutorials should be ready within a month. See some [examples](examples) t
 + [Spatial Transformer Networks on MNIST addition](examples/SpatialTransformer)
 
 ### Reinforcement Learning:
-+ [Deep Q-Network(DQN) variants on Atari games](examples/Atari2600)
-+ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](examples/OpenAIGym)
++ [Deep Q-Network(DQN) variants on Atari games](examples/DeepQNetwork)
++ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](examples/A3C-Gym)
 
 ### Unsupervised Learning:
 + [Several Generative Adversarial Network(GAN) variants, including DCGAN, Image2Image, InfoGAN](examples/GAN)
 
 ### Speech / NLP:
-+ [LSTM-CTC for speech recognition](examples/TIMIT)
-+ [char-rnn for fun](examples/char-rnn)
++ [LSTM-CTC for speech recognition](examples/CTC-TIMIT)
++ [char-rnn for fun](examples/Char-RNN)
 + [LSTM language model on PennTreebank](examples/PennTreebank)
 
 The examples are not only for demonstration of the framework -- you can train them and reproduce the results in papers.

examples/A3C-Gym/common.py

+../DeepQNetwork/common.py
\ No newline at end of file

examples/OpenAIGym/common.py

-../Atari2600/common.py
\ No newline at end of file

examples/README.md

@@ -16,15 +16,15 @@ 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](Atari2600)
-+ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](OpenAIGym)
++ [Deep Q-Network(DQN) variants on Atari games](DeepQNetwork)
++ [Asynchronous Advantage Actor-Critic(A3C) with demos on OpenAI Gym](A3C-Gym)
 
 ## Unsupervised:
 + [Generative Adversarial Network(GAN) variants, including DCGAN, Image2Image, InfoGAN](GAN)
 
 ## Speech / NLP:
-+ [LSTM-CTC for speech recognition](TIMIT)
-+ [char-rnn for fun](char-rnn)
++ [LSTM-CTC for speech recognition](CTC-TIMIT)
++ [char-rnn for fun](Char-RNN)
 + [LSTM language model on PennTreebank](PennTreebank)