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 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.
-
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.
-
Place the file in the course GitHub repository, in the
p1_navigation/
folder, and unzip (or decompress) the file.
Follow the instructions in Navigation.ipynb
to get started with training your own agent!
jupyter notebook Navigation.ipynb
Make sure to select the drlnd kernel before running the notebook.
The notebook contains all the necessary code for training the agent. Simply follow the instructions in the notebook to train the agent and evaluate its performance.
Results After training, the weights of the trained agent will be saved in a file called checkpoint.pth. You can use this file to load the trained agent and run it in the environment without needing to train it again.
The report.ipynb notebook contains a description of the learning algorithm used and the results of the training process.