This repository is detailed extensively in the accompanied Research Paper.

The current version of the Deep Q-Learning Network implimentation. It provides a convience wrapper for training an agent in any enviornment.

DQN algorithm applied to games from the popular benchmark Gym from OpenAI.

Visualizations of various model performance on different tasks.

RL models still in production that have no guarantee of their effectiveness.

Code samples found elsewhere on github, they may be used as a reference when updating files in future_models

sudo apt-get install python3.6

OpenAI's Universe environments are only supported on Linux and Mac distributions (does not work on Windows). If you only have a Windows OS you have the option to run CustomDQN on the OpenAI's Gym environments (see gym_environment_tests). I recommend using Python3.6 to execute the code in this repository.

Once you have python installed, you will need to install the required modules via pip:

pip install matplotlib numpy tensorflow keras gym universe

In order to use gym_enviornment_tests/LunarLander you will need to pip install gym[box2d] box2d-py

git clone https://github.com/nathanShepherd/Intelligent-Interface.git

sudo python mwob_Agent.py

Or you can observe the Agent control a Lunar Lander

python gym_environment_tests/LunarLander/lunarLander-CustomDQN.py

• Use DenseNet to improve classification accuracy

• Use a CRF or RNN/LSTM to help estimate the Q-Function relative to the current point in time

• Augment memory for efficient and prioritized experience replay