Author: Wes Brewer wes@computational.io
This code solves the Unity ML-Agents Banana Collector environment using a deep Q-network reinforcement algorithm implemented using PyTorch. In the Bananas environment, there are four possible actions:
- 0 - walk forward
- 1 - walk backward
- 2 - turn left
- 3 - turn right
The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction. A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana.
The Unity environment needs to be installed from one of the following links:
- Linux: https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Linux.zip
- Mac OSX: https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana.app.zip
- Windows (32-bit): https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Windows_x86.zip
- Windows (64-bit): https://s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Windows_x86_64.zip
This project uses Python 3 with the following required packages:
- numpy==1.14.3
- unityagents==0.4.0
- torch==1.0.0
- matplotlib==2.2.2
To install all the required packages, run:
python -r requirements.txt
The trained weights are saved as checkpoint.pth