Update README.md

Author: ChenYuSean

README.md

@@ -1,68 +1,3 @@
-## CrossyRoad 
-EXE is under Execuatable folder
-
-## Document
-More Document about MLagent is at https://github.com/Unity-Technologies/ml-agents/blob/release_19_docs/docs/Readme.md <br />
-Useful Doc:
-- [API Docs/Python API Documentation](https://github.com/Unity-Technologies/ml-agents/blob/release_19_docs/docs/Python-API-Documentation.md)
-- [API Docs/How to use the Python API](https://github.com/Unity-Technologies/ml-agents/blob/release_19_docs/docs/Python-API.md)
-- [Python Tutorial with Google Colab/Using a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/release_19_docs/colab/Colab_UnityEnvironment_1_Run.ipynb)
-- [Python Tutorial with Google Colab/Q-Learning with a UnityEnvironment](https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/release_19_docs/colab/Colab_UnityEnvironment_2_Train.ipynb) 
-
-## Installation
-1. create an enviroment with **Python 3.6 or 3.7**
-2. Install the pytorch from https://pytorch.org/get-started/locally/
-3. Install the mlagent with pip
-```
-python -m pip install mlagents==0.28.0
-```
-4. Install importlib-metadata
-```
-pip install importlib-metadata==4.4
-```
-More Installation Detail at https://github.com/Unity-Technologies/ml-agents/blob/release_19_docs/docs/Installation.md
-
-## Usage (Command Line)
-Run the MLAgent Default Model(PPO/SAC) by Anaconda command prompt under the folder with exe
-```
-mlagents-learn <config path> --env=<exe name> --run-id=<run_name>
-```
-It should be like
-```
-mlagents-learn config\player_config.yaml --env="CRML" --run-id=test
-```
-
-## Usage (Python)
-To load a Unity environment from a built binary file, put the file in the same directory
-as enviroment(exe), run:
-```python
-from mlagents_envs.environment import UnityEnvironment
-# This is a non-blocking call that only loads the environment.
-env = UnityEnvironment(file_name="CRML", seed=1, side_channels=[])
-# Start interacting with the environment.
-env.reset()
-behavior_names = env.behavior_specs.keys()
-...
-```
-more Details at https://github.com/Unity-Technologies/ml-agents/blob/release_19_docs/docs/Python-API.md
-
-## Action Space
-Continuous Action: 0 <br />
-Discrete Action: 1 <br />
-- Branch size: 5 <br />
-0: No Movement/1: Front/2: Back/3: Left/4: Right
-
-## Observation Space
-Total size: 60 <br />
-30 feature obsered and with 2 stacked vector.
-- size 2: Player Coordinate(X,Z)
-- size 4: The type of line which relative to player(previous,current,next two)<br />
-type 0: Grass, 1: Road, 2: Water
-- size 2: The Obstacles Coordinate(X,Z)<br />
-3 obstacle observed per line. 6 feature per line. Total 24 feature.
-
-## Changelogs
-- v2.1: Reward should add correctly when beating the high score
-- v2.0: Observation size now change to 60.<br/>
-add Player coordinate, Line type, Obstacle coordinate to observation
-- v1.0: Executable Create. Observation space size = 3
+## CrossyRoad Unity
+Unity source code <br/>
+Find the Executable File [here](https://github.com/Introduction-to-Machine-Learning-Team4/Executable)