ROBOTICS

REINFORCEMENT LEARNING

Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization

March 25, 2021

Abstract

Bayesian optimization (BO) is a popular approach to optimize expensive-to-evaluate black-box functions. A significant challenge in BO is to scale to high-dimensional parameter spaces while retaining sample efficiency. A solution considered in existing literature is to embed the high-dimensional space in a lower-dimensional manifold, often via a random linear embedding. In this paper, we identify several crucial issues and misconceptions about the use of linear embeddings for BO. We study the properties of linear embeddings from the literature and show that some of the design choices in current approaches adversely impact their performance. We show empirically that properly addressing these issues significantly improves the efficacy of linear embeddings for BO on a range of problems, including learning a gait policy for robot locomotion.

Download the Paper

AUTHORS

Written by

Ben Letham

Roberto Calandra

Akshara Rai

Eytan Bakshy

Publisher

NeurIPS

Research Topics

Reinforcement Learning

Robotics

Related Publications

January 06, 2024

RANKING AND RECOMMENDATIONS

REINFORCEMENT LEARNING

Learning to bid and rank together in recommendation systems

Geng Ji, Wentao Jiang, Jiang Li, Fahmid Morshed Fahid, Zhengxing Chen, Yinghua Li, Jun Xiao, Chongxi Bao, Zheqing (Bill) Zhu

January 06, 2024

December 11, 2023

REINFORCEMENT LEARNING

CORE MACHINE LEARNING

TaskMet: Task-driven Metric Learning for Model Learning

Dishank Bansal, Ricky Chen, Mustafa Mukadam, Brandon Amos

December 11, 2023

October 12, 2023

ROBOTICS

SLAP: Spatial-Language Attention Policies

Christopher Paxton, Jay Vakil, Priyam Parashar, Sam Powers, Xiaohan Zhang, Yonatan Bisk, Vidhi Jain

October 12, 2023

October 01, 2023

REINFORCEMENT LEARNING

CORE MACHINE LEARNING

Q-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-Snapshots

Wei Hung, Bo-Kai Huang, Ping-Chun Hsieh, Xi Liu

October 01, 2023

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.