wjlgatech / drl-dqn-navigation

Navigation by Deep Q-network

Objectives

For this project, an agent need to be trained to navigate in a large, square world and collect bananas.

Environment Overview

State Space: The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction.

Action Space: Four discrete actions are available, corresponding to:

• 0 - move forward.
• 1 - move backward.
• 2 - turn left.
• 3 - turn right.

Reward: 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.

Problem Solved: The task is episodic. The environment is considered solved when the agent get an average score of +13 over 100 consecutive episodes.

Instructions

STEP 0: set up dependencies

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

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

- __Linux__ or __Mac__: 
```bash
conda create --name drlnd python=3.6
source activate drlnd
```
- __Windows__: 
```bash
conda create --name drlnd python=3.6 
activate drlnd
```

B. Follow the instructions in this repository to perform a minimal install of OpenAI gym.
- Next, install the classic control environment group by following the instructions here. - Then, install the box2d environment group by following the instructions here.

C. Clone the repository (if you haven't already!), and navigate to the python/ folder. Then, install several dependencies.

git clone https://github.com/udacity/deep-reinforcement-learning.git
cd deep-reinforcement-learning/python
pip install .

D. Create an IPython kernel for the drlnd environment.

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

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

STEP 1: Clone Repository

Open up a terminal, go to the directory of your choice and clone the repository

git clone https://github.com/wjlgatech/DRL-dqn-navigation.git .

STEP 2: Download 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 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 same folder as the downloaded repository, and unzip (or decompress) the file.

STEP 3: Run Codes

One way is that you open the Navigation.ipynb notebook to follow instructions there:

jupyter notebook Navigation.ipynb

Another way is that you train the agents with

python train.py

and test the trained agents with

python test.py

Code Overview

The code consists of the following modules

Navigation.ipynb - the main notebook
dqn_agent.py - defines the Agent that is to be trained
checkpoint.pth - is the final trained dqn network
train.py - train the dqn agent
test.py - test the performance of the trained agent
Navigation_Report.ipynb - the project report

Results

DNQ is able to solve the environment in 2866 episodes with average Score: 13.05