Dependency Structure Search Bayesian Optimization for Decision Making Models

TL;DR

This paper introduces Dependency Structure Search Bayesian Optimization, a method designed to efficiently optimize complex, multi-agent decision making models with high-dimensional parameter spaces, especially under sparse or uninformative feedback conditions.

Contribution

The paper proposes a novel multi-layered architecture and an optimization method that improves scalability and performance in high-dimensional, multi-agent decision models with sparse rewards.

Findings

Outperforms existing methods in sparse reward scenarios

Achieves improved regret bounds

Demonstrates empirical success on complex models

Abstract

Many approaches for optimizing decision making models rely on gradient based methods requiring informative feedback from the environment. However, in the case where such feedback is sparse or uninformative, such approaches may result in poor performance. Derivative-free approaches such as Bayesian Optimization mitigate the dependency on the quality of gradient feedback, but are known to scale poorly in the high-dimension setting of complex decision making models. This problem is exacerbated if the model requires interactions between several agents cooperating to accomplish a shared goal. To address the dimensionality challenge, we propose a compact multi-layered architecture modeling the dynamics of agent interactions through the concept of role. We introduce Dependency Structure Search Bayesian Optimization to efficiently optimize the multi-layered architecture parameterized by a large number of parameters, and show an improved regret bound. Our approach shows strong empirical results under malformed or sparse reward.