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.
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.
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.
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.