Auto Dino

A 2013 publication by DeepMind titled ‘Playing Atari with Deep Reinforcement Learning’ introduced a new deep learning model for reinforcement learning, and demonstrated its ability to master difficult control policies for Atari 2600 computer games, using only raw pixels as input. In this repository, a Deep Convolutional Neural Network will learn to play Google Chrome's Dino Run game by learning action patterns using a model-less Reinforcement Learning Algorithm using Nadam Optimizer. Moreover, this is using the latest TensorFlow 2.0. So there will be no version confilts while running this project on TF 2.0.

Requirements

• Set up a python environment with required dependencies installed.
• If you are familiar with Docker, you can use this container that comes preinstalled with everything you need (Coming Soon).

Usage

For Python Environment:

1. Downloading this Respository

Start by downloading or clone the repository:

$ git clone https://github.com/Dexter2389/Auto-Dino.git
$ cd Auto-Dino

2. Install Dependencies and get Chromedriver

• If you are running this without the Docker image, you will need to get the chromedriver and place it in the working directory. This is a requirement to make the python script interact wih the Google Chrome. You can download the and extract by running:

$ cd Auto-Dino
$ wget https://chromedriver.storage.googleapis.com/2.41/chromedriver_linux64.zip
$ unzip chromedriver_linux64.zip

• You will also need to install specific python dependencies for this project:

pip install -r requirements.txt

3. Start the training

1. Run init_cache.py first time to initialize the file system structure.

2. Run RLDinoRunTF_2.0.py to start the training of the Dino Run game.

4. Results

Run DinoTrainingProgress.py to see the results of the training process

For Docker Container (Coming Soon!):

1. xxxxx

Acknowledgements

• Thanks to Ravi Munde for his awesome article because which this project as possible