ImmanuelXIV/dqn-navigate

Reinforcement Learning | Deep Q-Network (DQN) agent | Value-Based Method | PyTorch | Unity Banana environment

Navigation via DQN

This repository contains the implementation of a DQN algorithm and an agent to solve the Bananas environment. The simulation environment stems from Unity ML-Agents.

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. This repository is part of a Udacity Deep Reinforcement Learning Nanodegree program submission.

Dependencies

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

1. Create and activate a new conda environment with Python 3.6. If you don't have Conda, click here for Conda installation instructions

   • Linux or Mac:

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

   • Windows:

   conda create --name navi python=3.6 
   activate navi

2. Clone this repository, and navigate to the dqn-navigation/python/ folder. Then, install several dependencies.

git clone https://github.com/ImmanuelXIV/dqn-navigation.git
cd dqn-navigation/python
pip install .

3. Download the Banana environment from one of the links below. You need only select the environment that matches your operating system. Place it in the dqn-navigation/ dir, decompress it and change the file_name accordingly.

Downloads

• Mac OSX: click here
• Linux: click here
• Windows (32-bit): click here
• Windows (64-bit): click here

Paths

• Mac: "path/to/Banana.app"
• Windows (x86): "path/to/Banana_Windows_x86/Banana.exe"
• Windows (x86_64): "path/to/Banana_Windows_x86_64/Banana.exe"
• Linux (x86): "path/to/Banana_Linux/Banana.x86"
• Linux (x86_64): "path/to/Banana_Linux/Banana.x86_64"
• Linux (x86, headless): "path/to/Banana_Linux_NoVis/Banana.x86"
• Linux (x86_64, headless): "path/to/Banana_Linux_NoVis/Banana.x86_64"

(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 "headless" version of the environment. You will not be able to watch the agent without enabling a virtual screen, but you will be able to train the agent. (To watch the agent, you should follow the instructions to enable a virtual screen, and then download the environment for the Linux operating system above.)

4. Create an IPython kernel for the navi environment.

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

5. Run the following code and follow the instructions in the notebook (e.g. run all)

cd dqn-navigation/
jupyter notebook

6. Before running code in the Navigation.ipynb notebook, change the kernel to match the navi environment by using the drop-down Kernel menu in the toolbar.