Update README.md

Author: tcmxx

README.md

@@ -2,27 +2,32 @@
 
 - It is an extension of [Unity ML agent](https://github.com/Unity-Technologies/ml-agents) for deep learning, primarily reinforcement learning, with in-editor/in-game training support. It also provides interface for another optimization algorithm called MAES.
 
-- It uses a modified version of [KerasSharp](https://github.com/tcmxx/keras-sharp) and [TensorflowSharp](https://github.com/migueldeicaza/TensorFlowSharp) as backend. No python is needed for model building/evaluation/training. You can even build a standalone with training capability.
+- It uses a modified version of [KerasSharp](https://github.com/tcmxx/keras-sharp) and [TensorflowSharp](https://github.com/migueldeicaza/TensorFlowSharp) as backend. No python is needed for model building/evaluation/training. You can even build a standalone(an actual playable game!) with training capability.
 
-- This repo is made for Aalto University's Computational Intelligence in Games course. [The original materials](https://github.com/tcmxx/CNTKUnityTools) are made with [CNTK](https://github.com/Microsoft/CNTK). Now it is remade with [Tensorflow](https://github.com/tensorflow/tensorflow). It will also be my master's thesis hopefully.
+- This repo is made for Aalto University's Computational Intelligence in Games course. [The original materials](https://github.com/tcmxx/CNTKUnityTools) are made with [CNTK](https://github.com/Microsoft/CNTK). Now the course is updated to another course called [Computational Intelligence in Media](https://onlinelearning.aalto.fi/pilot/intelligent-computational-media/) whcih includes two parts: Audio and Games. The material game part for is remade here with [Tensorflow](https://github.com/tensorflow/tensorflow) and [Unity ML agent](https://github.com/Unity-Technologies/ml-agents). It will also be my master's thesis hopefully.
 
 - Note: This project is still in development. Don't use this unless you know that you have time to check the sourcecodes!
 
+
 ## Features:
 * Use your already made Unity ML-Agent, but enable learning in Unity editor/build without python or extra coding.
 * Reinforcement learning(using PPO) and supervised learning.
-* Evolution Strategy (using Matrix Adaption Evolution Strategy(MEAS))
+* Evolution Strategy (using Covariance Matrix Adaption Evolution Strategy(CMA-ES))
 * Examples provided.
 
-## Documentation
-For more information including installation and usage instructions, go to [Document](Documents/Readme.md).
-
-## Tested Unity Version: 2018.1.6f1
+# Requirements: 
+[Unity ML agent v0.5](https://github.com/Unity-Technologies/ml-agents) 
+Unity 2018.1.6f1(Should be working with some of the newer versions as well).
 
 ## Platforms:
-Windows is almost fully supported. If you want to use GPU, CUDA and cuDNN are needed(Please google CUDA v9.0 and cudnn v7 and install them). Mac should be fully supported if I have a Mac to build, but now it does not have Concat Gradient. Mac does not support GPU. Linux is not tested at all.
+- Windows is almost fully supported. If you want to use GPU, CUDA and cuDNN are needed(Please google CUDA v9.0 and cudnn v7 and install them). 
+- Mac should be fully supported if I have a Mac to build, but now it does not have Concat Gradient, which means the agent can not have both visual and vector observations at the same time, and discrete action branching is not supported neither. Mac does not support GPU. - Linux is not tested at all.
+- Android does not support any type of gradient/training. But you can use trained neural network on it. 
+- IOS is not tested a all.
 
-Android does not support any type of gradient/training. IOS is not tested a all.
+## Documentation
+- Installation: https://github.com/tcmxx/UnityTensorflowKeras/blob/master/Documents/Installation.md
+- For more information including installation and usage instructions, go to [Document](Documents/Readme.md).
 
 ## Future Plan:
 We plan to keep this repo updated with latest game related machine learning technologies for the course every year.
@@ -32,7 +37,7 @@ Possible future plans/contributions:
 * More benchmark environments.
 * Better API for in game usage.
 * More algorithms including: Deep Q Learning, Deep Mimic, Evolved Policy Gradient, Genetic Algorithms and so on.
-* Improving the logging tool or using Tensorboard in c#.
+* Improving the logging tool.
 * Graphic editor for neural network architecture
 
 ## License