This project contains a solution to the first project of Udacity Deep Reinforcement Learning.
For this project, you will train an agent to navigate (and collect bananas!) in a large, square world.
A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana. Thus, the goal of your agent is to collect as many yellow bananas as possible while avoiding blue bananas.
The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction. Given this information, the agent has to learn how to best select actions. Four discrete actions are available, corresponding to:
0- move forward.1- move backward.2- turn left.3- turn right.
The task is episodic, and in order to solve the environment, your agent must get an average score of +13 over 100 consecutive episodes.
It is helpful to check the repository below for details.
- Python 3.6
- Unity
And then to install python dependencies.
pip install -r requirements.txt
Then you should be able to run jupyter notebook and view Navigation.ipynb.
The code for the Model and Agent are in model.py and agent.py, respectively.
Run each cell of Navagation.ipynb.
You can also run run.py to pop up the Unity Agent directly and check the behavior with the already trained weight.
python run.py