This project trains 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. The goal of the 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 to consider the environment solved, the agent must get an average score of +13 over 100 consecutive episodes.
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Clone this git repository on your local machine
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Setup a Jupyter notebook on your local machine. For details on how to do that, here is a resource that may be helpful: https://www.dataquest.io/blog/jupyter-notebook-tutorial/
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Download the environment from one of the links below. You need only select the environment that matches your operating system:
- Linux: click here
- Mac OSX: click here
- Windows (32-bit): click here
- Windows (64-bit): click here
(For Windows users) Check out this link if you need help with determining if your computer is running a 32-bit version or 64-bit version of the Windows operating system.
(For AWS) If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link to obtain the environment.
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Place this file in the directory that you cloned the git repositiory, and unzip (or decompress) the file.
Open the Navigation.ipynb notebook file, and run the notebook to train the agent