ASGF

This repository contains the source code reproducing the numerical examples presented in the paper An adaptive stochastic gradient-free approach for high-dimensional blackbox optimization.

Requirements

An appropriate environment for these pieces of code can be created using the environment yml file included. To create a conda environment use:

conda env create -f environment.yml

Then to activate the constructed environment use:

conda activate asgf

NOTE: The creation of the asgf conda environment may take up to 5-7 minutes.

Example usage for functional optimization

python -m optimize --fun=ackley --dim=10 --algo=asgf --sim=100

• fun -- a string for the name of function to be minimizer. Implemented functions are in tools/function.py
• dim -- input dimension of function
• algo -- name of algorithm to use to minimizer the function
• sim -- number of simulations or trials. Each simulation begins at a different inital point with different random seeding throughout.

Examlpe usage for reinforcement learning

mpiexec -n 8 python -m train --env_name=InvertedPendulumBulletEnv-v0 --algo=asgf --hidden_sizes=8,8

• env_name -- name of gym environment. Must be already registered in gym or in pybullet_envs
• algo -- name of algorithm to use to train the agent
• hidden_sizes -- comma seperated values which represent the number of nodes to use on the hidden layers.