Project 1: Navigation
Introduction
In this project, an agent is trained 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, the agent must get an average score of +13 over 100 consecutive episodes.
Getting Started
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If you are using Windows you can use the provided binaries. Otherwise, download the appropriate 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 the file in the root folder of this repository and unzip (or decompress) the file.
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You can use the environment.yml to install all dependenies if you're using conda.
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IMPORTANT: Make sure you have version 0.4 of mlagents! pip install mlagents==0.4
Instructions
The last part of Navigation.ipynb contains an adapation of DQN to train a successful agent! The file checkpoint.pth contains the model of a trained agent.