whbrewer / dqn

Deep Q-learning with Unity ML-Agents toolkit

Author: Wes Brewer [e-mail]

Introduction

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.

How to Install

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

Notes

The trained weights are saved as checkpoint.pth