diff --git a/README.md b/README.md
index 94415bc..8021b6d 100644
--- a/README.md
+++ b/README.md
@@ -23,6 +23,29 @@ Asynchronous algorithms below are removed in current version but can be found in
 
 Support for PyTorch v0.3.x can be found in [v0.2](https://github.com/ShangtongZhang/DeepRL/releases/tag/v0.2). Note all the figures are generated via this version. After the upgrade to PyTorch v0.4.0, I have only tested the classical control tasks. 
 
+# Dependency
+* MacOS 10.12 or Ubuntu 16.04
+* PyTorch v0.4.0
+* Python 3.6, 3.5 or 2.7 (deprecated)
+* Core dependencies: `pip install -e .`
+* Optional: [Roboschool](https://github.com/openai/roboschool), [DeepMind Control Suite](https://github.com/deepmind/dm_control)+[DMControl2Gym](dm_control2gym)
+
+# Usage
+
+```examples.py``` contains examples for all the implemented algorithms
+
+Please use this bibtex if you want to cite this repo
+```
+@misc{deeprl,
+  author = {Shangtong, Zhang},
+  title = {Modularized Implementation of Deep RL Algorithms in PyTorch},
+  year = {2018},
+  publisher = {GitHub},
+  journal = {GitHub Repository},
+  howpublished = {\url{https://github.com/ShangtongZhang/DeepRL}},
+}
+```
+
 # Curves
 > Curves for CartPole are trivial so I didn't place it here, and there isn't any fixed random seed. The curves are generated in the same manner as OpenAI baselines (one run and smoothed by recent 100 episodes)
 ## DQN
@@ -49,6 +72,7 @@ Support for PyTorch v0.3.x can be found in [v0.2](https://github.com/ShangtongZh
 
 ## OC
 ![Loading...](https://raw.githubusercontent.com/ShangtongZhang/DeepRL/master/images/option_critic_pixel_atari-180417-092617.png)
+
 This is my synchronous option-critic implementation, not the original one.
 
 ## Action Conditional Video Prediction
@@ -58,29 +82,6 @@ This is my synchronous option-critic implementation, not the original one.
 
 Prediction is sampled after 110K iterations, and I only implemented one-step training
 
-# Dependency
-* MacOS 10.12 or Ubuntu 16.04
-* PyTorch v0.4.0
-* Python 3.6, 3.5 or 2.7 (deprecated)
-* Core dependencies: `pip install -e .`
-* Optional: [Roboschool](https://github.com/openai/roboschool), [DeepMind Control Suite](https://github.com/deepmind/dm_control)+[DMControl2Gym](dm_control2gym)
-
-# Usage
-
-```examples.py``` contains examples for all the implemented algorithms
-
-Please use this bibtex if you want to cite this repo
-```
-@misc{deeprl,
-  author = {Shangtong, Zhang},
-  title = {Modularized Implementation of Deep RL Algorithms in PyTorch},
-  year = {2018},
-  publisher = {GitHub},
-  journal = {GitHub Repository},
-  howpublished = {\url{https://github.com/ShangtongZhang/DeepRL}},
-}
-```
-
 # References
 * [Human Level Control through Deep Reinforcement Learning](https://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)
 * [Asynchronous Methods for Deep Reinforcement Learning](https://arxiv.org/abs/1602.01783)