azzeddineCH / Udacity-ND-DRL-Navigation

a solution for the udacity DeepRL Nanodegree Navigation project

Udacity Deep Reinforcement Learning Nanodegree - Project 1: Navigation

Introduction

For this Udacity project, I used a Deep Q-Network to solve a Unity environment which requires the agent to collect yellow bananas and avoid blue bananas.

In this environment, a reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana. 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.M
• 3 - turn right.

The task is episodic, and in order to solve the environment, the agent must get an average score of +13 over 100 consecutive episodes.

Getting Started

Codes were written using Python3.6.2, run this to install necessary packages:

pip install -r requirements.txt

The Unity environment only runs on Macs, for Windows users, download the Windows 32-bit version here.

Please follow the instruction in the Udacity DRLND Instructions on setting up the environment.

Instructions

• Run all the block cells Report.ipynb to initiate the environment and go through the trainings.
• model.py defines the Deep Q-Network's architecture.
• den_agent.py defines the DQN agent class.
• checkpoint.pth is the saved Deep Q-Network weights after training.