Implementation of Fisher-Adaptive Natural Gradient

Package contents:

This repository contains two main components.

• adacurv/torch: this module contains PyTorch code for the adaptive NGD methods we study along with utilities for Fisher-vector products, CG optimization, Lanczos iteration, and line search.

• experiments: this subdirectory contains the code used to run the experiments in the paper.

  • mnist and svhn: code used for the supervised training experiments.
  • mjrl: code adapted from an existing MuJoCo RL library, MJRL, to accommodate PyBullet and our new optimizers.

  These experiments all contain a run.py file which was used to launch our experiments. See below for a quick-start script.

Installation:

1. Clone this package git clone https://github.com/tpbarron/adacurv.git.
2. Add the path to this folder to your python path export PYTHONPATH=$PYTHONPATH:/path/to/adacurv/.
3. Install python dependencies pip install -r requirements.txt.

Dependencies

The majority of the dependencies can be installed using the requirements file. The code has been run using python3.[5,6] and has not been tested with python2.

Running the example scripts:

For a faster start we include a quick_start.py file in each of the mnist and mjrl experiment directories that runs a single sample experiment. Once this has finished the data can be plotted with the plot_quick_start.py script.