This repository is a Python implementation of the MuZero algorithm. It is based upon the pre-print paper and the pseudocode describing the Muzero framework. Neural computations are implemented with Tensorflow.

You can easily train your own MuZero, more specifically for one player and non-image based environments (such as CartPole). If you wish to train Muzero on other kinds of environments, this codebase can be used with slight modifications.

DISCLAIMER: this code is early research code. What this means is:

• Silent bugs may exist.
• It may not work reliably on other environments or with other hyper-parameters.
• The code quality and documentation are quite lacking, and much of the code might still feel "in-progress".
• The training and testing pipeline is not very advanced.

We run this code using:

• Conda 4.7.12
• Python 3.7
• Tensorflow 2.0.0
• Numpy 1.17.3

This code must be run from the main function in muzero.py (don't forget to first configure your conda environment).

To train a model, please follow these steps:

1. Create or modify an existing configuration of Muzero in config.py.
2. Call the right configuration inside the main of muzero.py.
3. Run the main function: python muzero.py.

To train on a different environment than Cartpole-v1, please follow these additional steps:

1) Create a class that extends AbstractGame, this class should implement the behavior of your environment. For instance, the CartPole class extends AbstractGame and works as a wrapper upon gym CartPole-v1. You can use the CartPole class as a template for any gym environment.

2) This step is optional (only if you want to use a different kind of network architecture or value/reward transform). Create a class that extends BaseNetwork, this class should implement the different networks (representation, value, policy, reward and dynamic) and value/reward transforms. For instance, the CartPoleNetwork class extends BaseNetwork and implements fully connected networks.

3) This step is optional (only if you use a different value/reward transform). You should implement the corresponding inverse value/reward transform by modifying the loss_value and loss_reward function inside training.py.

This implementation differ from the original paper in the following manners:

• We use fully connected layers instead of convolutional ones. This is due to the nature of our environment (Cartpole-v1) which as no spatial correlation in the observation vector.
• We don't scale the hidden state between 0 and 1 using min-max normalization. Instead we use a tanh function that maps any values in a range between -1 and 1.
• We do use a slightly simple invertible transform for the value prediction by removing the linear term.
• During training, samples are drawn from a uniform distribution instead of using prioritized replay.
• We also scale the loss of each head by 1/K (with K the number of unrolled steps). But, instead we consider that K is always constant (even if it is not always true).