For this project, a slightly modified version of the Unity "Banana Collectors" environment was used. The goal of this environment is to collect as many yellow bananas as possible. +1 point is awarded for each yellow banana collected. -1 points are awarded for each purple banana collected. The state space is defined by a 37d vector, where 36 of the values represent rays showing what is in front of the agent, and 1 value represents the speed of the agent. There are 4 actions the agent can take: left, right, forwards, or backwards. The environment is considered "solved" when an average score of 13 can be achieved over the last 100 episodes.
In order to ensure that your environment has all the necessary dependencies, follow the steps here
Next, you will need to download the environment used for this exercise.
- Linux: click here
- Mac OSX: click here
- Windows (32-bit): click here
- Windows (64-bit): click here
Unzip the file and take note of the unzipped path.
Run through cells in Banana.ipynb to train an agent. Parameters regarding the agent can be found in agent.py, while the model architecture can be found in model.py.