Reinforced In-Context Black-Box Optimization

This repository contains the Python code for Reinforced In-Context Black-Box Optimization (RIBBO), a method to reinforce-learn a BBO algorithm from offline data in an end-to-end fashion.

Requirements

• Python == 3.10
• PyTorch == 2.0.1
• offlinerllib==0.1.1
• utilsrl==0.6.3
• google-vizier==0.1.9
• gpytorch==1.11
• botorch=0.9.4

File Structure

• algorithms directory is the main implement of RIBBO, BC, BC Filter, and OptFormer
• data_gen directory is the implement of behavior algorithms and data collection
• datasets directory provides the interface of the offline datasets
• problems directory is the implement of the benchmark problems
• scripts directory provides some scripts for reproduction

Usage

Run bash scripts/run_main.sh to evaluate RIBBO and other baselines

Datasets

The datasets were not released during the review stage, but we provided detailed guidelines in our paper and the code for data generation. We will open-source the datasets and code after the final decision