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 a ray-based perception of objects around the 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 backwards.2- turn left.3- turn right.
The task is episodic, and to solve the environment, your agent must get an average score of +13 over 100 consecutive episodes.
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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 the file in the root of this repository and unzip (or decompress) the file.
To set up your python environment to run the code in this repository, follow the instructions below.
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Create (and activate) a new environment with Python 3.6.
- Linux or Mac:
conda create --name drlnd python=3.6 source activate drlnd- Windows:
conda create --name drlnd python=3.6 activate drlnd
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Once you have the repository cloned, navigate to the
python/folder. Then, install several dependencies.
pip install .python -m ipykernel install --user --name=drlnd- Before running code in a notebook, change the kernel to match the
drlndenvironment by using the drop-downKernelmenu.
To run the notebook use the following command in the root of this repository
jupyter notebook Navigation.ipynbThis will launch the jupyter notebook interface with the Navigation.ipynb. Once this has loaded, run all the cells (Cell > Run all). It may take some time as it will run through all 10 configurations.