mfts/RLND-Navigation-P1

Project 1: Training an agent to navigate a video game – for Udacity Deep Reinforcement Learning Nanodegree

Training an agent to navigate

Introduction

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.

Setup

Installation

This project contains a Jupyter notebook and several Python files.

To set up your python environment to run the code in this repository, follow the instructions below.

1. Create (and activate) a new environment with Python 3.6.

   conda create --name drlnd python=3.6
   source activate drlnd
   

2. Clone repository and install python dependencies

   git clone REPO_NAME
   cd REPO_NAME
   pip install ./python
   

3. Create an IPython kernel for the drlnd environment.

   python -m ipykernel install --user --name drlnd --display-name "drlnd"
   

4. Before running code in a notebook, change the kernel to match the drlnd environment by using the drop-down Kernel menu.

   

Set up Unity-ML Agents Environment

1. 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](https://support.microsoft.com/en-us/help/827218/ how-to-determine-whether-a-computer-is-running-a-32-bit-version-or-64) 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](https://github.com/ Unity-Technologies/ml-agents/blob/master/docs/Training-on-Amazon-Web-Service.md)), then please use [this link](https:// s3-us-west-1.amazonaws.com/udacity-drlnd/P1/Banana/Banana_Linux_NoVis.zip) to obtain the environment.

2. Place the file in the root of the Github repository's folder (RLND-Navigation-P1), and unzip (or decompress) the file.

Instructions

Navigate to Navigation.ipynb. You have the options either to

• a) train your own agent based on my model
• b) see my trained agent in action

For a), please execute 1-5. For b), please execute 1-4 and 6.

For improvments to the agent or replay of improved agents, please modify the following files:

• Dueling DQN:

   agent.py:3
   # before
   from model import QNetwork as QNetwork
   # after
   from model import DuelQNetwork as QNetwork
  

• Double DQN:

   agent.py:63
   # before
   self.learn(experiences, GAMMA)
   # after
   self.doublelearn(experiences, GAMMA)
  

• Dueling Double DQN: change both lines as instructed above