DeepRL-ND-Navigation

Report

My code from scratch:

Double Double DQN-Dueling: Navigation-DDQN-Dueling.ipynb

DDQN: Navigation-DDQN.ipynb

DuelingDQN: Navigation-Dueling-DQN.ipynb

DQN: Navigation-DQN.ipynb

Project 1: Navigation

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.

Getting Started

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 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), then please use this link to obtain the environment.

2. Place the file in the DRLND GitHub repository, in the p1_navigation/ folder, and unzip (or decompress) the file.

Installation

Install the package requirements: !pip -q install ./python

Instructions

(Option1)

• Follow the instructions in Navigation-DDQN-Dueling.ipynb to train and run the agent!

$ git clone https://github.com/bmaxdk/DeepRL-ND-Navigation.git
$ cd DeepRL-ND-Navigation

Use jupyter notebook to open Navigation-DDQN-Dueling.ipynb

(Option2)

• Download all the file in libs
  • main.py: Simulae running agent
  • agent.py: Agent class
  • doubleDQN.py: DDQN model and train
  • network.py: Dueling Network model
  • replayBuffer.py: ReplayBuffer class (memory)
• Make sure checkpoint.pth and Banana_Linux are in same directory

$ git clone https://github.com/bmaxdk/DeepRL-ND-Navigation.git
$ cd DeepRL-ND-Navigation/libs

• To run program in terminal:

$ python main.py --environment/Banana_Linux/Banana.x86_64 

• To train program in terminal:

$ python doubleDQN.py --environment/Banana_Linux/Banana.x86_64