Project Overview: 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 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 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.
The Environment Follow the instructions below to explore the environment on your own machine! You will also learn how to use the Python API to control your agent.
Step 1: Clone the DRLND Repository
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.
Step 2: Download the Unity Environment
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.
Step 3: Explore the Environment After you have followed the instructions above, open Navigation.ipynb (located in the p1_navigation/ folder in the DRLND GitHub repository) and experiment to learn how to use the Python API to control the agent.