AlphaZero

Implementation of the AlphaZero algorithm

Introduction

This repo contains:

• a simple but working implementation of the AlphaZero algorithm
• an agent that uses the AlphaZero algorithm to play an openAI gym game (CartPole-v1)

Project details

The code is an addition to the MCTS algorithm implementation.

This is an implementation of an agent that uses an AlphaZero implementation in order to play the openAI gym game of CartPole.

Getting Started

Execute the code in the notebook to train the agent!

Dependencies

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

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

   • Linux or Mac:

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

   • Windows:

   conda create --name AlphaZero python=3.6 
   activate AlphaZero

2. Clone the repository, and then, install the required packages (see requirements).

git clone https://github.com/ciamic/AlphaZero.git

4. Create an IPython kernel for the AlphaZero environment.

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

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

Requirements

• Python 3
• numpy
• matplotlib
• gym
• Tensorflow