This assignment trains an agent for use in the unity environment trying to eat yellow bananas while avoiding blue bananas.
There are 4 actions the agent can take (forward, backward, turn left, turn right) for a total action space of 4 dimensions The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around the agent's forward direction. The environment is considered solved if the agent can attain an average score of >13 for 100 consecutive episode.
My agents score
Please follow the instructions in the DRLND GitHub repository! to set up your Python environment. These instructions can be found in README.md at the root of the repository. By following these instructions, you will install PyTorch, the ML-Agents toolkit, and a few more Python packages required to complete the project.
(For Windows users) The ML-Agents toolkit supports Windows 10. While it might be possible to run the ML-Agents toolkit using other versions of Windows, it has not been tested on other versions. Furthermore, the ML-Agents toolkit has not been tested on a Windows VM such as Bootcamp or Parallels.
For this project, you will not need to install Unity - this is because we have already built the environment for you, and you can download it 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!
Then, place the file in the p1_navigation/ folder in the DRLND GitHub repository, and unzip (or decompress) the file.
(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.
After you have followed the instructions above, open Navigation.ipynb (located in the p1_navigation/ folder in the DRLND GitHub repository) and follow the instructions to learn how to use the Python API to control the agent.
To run the code in the notebook, simply open it in Jupyter and run each cell in order. If you are using a local environment instead of the udacity workspace you can ignore the first cell.