Solving black-box global optimization problems efficiently across domains remains challenging especially for large scale optimization problems. Bayesian optimization has obtained important success as a black box optimization technique based on surrogates, but it still suffers when applied to large scale heterogeneous landscapes. Recent approaches have proposed non-myopic approximations and partitioning of the input domain into subregions to prioritize regions that capture important areas of the solution space. A Multi Agent Rollout formulation of Bayesian optimization (MAroBO) that partitions the input domain among finite set of agents for distributed sampling is proposed. This is the official implementation of MAroBO.
MAroBO constructs an m-ary tree structure where:
- Active regions are occupied by agents
- Abandoned regions become inactive and taken over by a dedicated agent
- Intuitively speaking, the existense of a 'hyper-edge' among agents facilitates decision-making.
We use the Poetry tool which is a dependency management and packaging tool in Python. It allows you to declare the libraries your project depends on and it will manage (install/update) them for you. Please follow the installation of poetry at https://python-poetry.org/docs/#installation
After you've installed poetry, you can install all the dependencies by running the following command in the root of the project:
poetry install
poetry run python demos/test_1d.py
poetry run python tests/log_test.py Test_internalBO.rastrigin
Look at the tests for more details.
Output receives a dictionary containing history and optimization_time.