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README.md

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+# PPO-Mario-Bros-Tensorflow-2
+A modular implementation for Proximal Policy Optimization in Tensorflow 2 using Eagerly Execution for the Super Mario Bros enviroment.
+
+Requeirements:
+- Tensorflow 2
+- OpenCV
+- OpenIA gym
+- Super Mario Bros NES, developed by Kautenja
+
+How to use:
+Clone the repository,
+
+For training run:
+!python -c 'from Main import train; test(True)'
+The argument of test enable the load of the trained model.
+
+For testing the model:
+!python -c 'from Main import test; test(10,0)'
+Where the first argument of test is the number of episodes to test the model, and the second is the number of the enviroment to test,
+for the code the enviroments of test are the next ones:
+0 : SuperMarioBros-1-1-v0
+The first level of the first world
+1 : SuperMarioBros-1-2-v0 
+The second level of the first world
+2 : SuperMarioBros-1-3-v0
+The third level of the first world
+3 : SuperMarioBros-2-2-v0
+The second level of the second world