Deep Q-Learning

This project is an implementation of a Q-learning algorithm for solving unity or gym environments.

Required packages:

• (gym if solving gym environment)
• numpy
• python (version 3.6)
• pytorch
• unityagents if solving unity environment

Required files:

The unity exe file has to be inside this folder; here is an environment called "Bananas.exe" included.

Bananas environment:

The included Bananas environement is an environment, in which the agent moves inside a square. Inside the square, there are yellow bananas giving a reward of 1 and blue bananas giving a reward of -1. The state space consists of 37 dimensions and 4 actions can be taken (move forward, move backwards, turn left, turn right). The environment is considered solved, when an average score of 13.0 over 100 episodes is reached.

Starting the program:

• hyperparameters and settings for the algorithm can be changed in the file "Hyperparameter.py". -> for viewing a trained agent set "LOAD" to True,"FILENAME_FOR_LOADING" to the name of the files of the model weights(without "_model_local.pth" or "_model_target.pth"), "EPS_START" to 0.01 and for unity "ENV_TRAIN" to False. -> for training a new agent set "LOAD" to False, "EPS_START" to 1.00 and for unity "ENV_TRAIN" to True and if you want to save the model weights, set "Save" to True and "FILENAME_FOR_SAVING" to the name for the weight files
• use the file "Main.py". -> comment out the method you want to start (either gym or unity). -> run the file "Main.py".
• other gym or unity environments can be used as well. Note that you cannot load a trained version for new environments until one is saved.
• changes in all other files are not recommended.