Dependency Structure Search Bayesian Optimization for Decision Making Models

TMLR Paper2596 Authors

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 give the first improved regret bound in additive high-dimensional Bayesian Optimization since Mutny and Krause (2018). Our approach shows strong empirical results under malformed or sparse reward.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Nino_Vieillard1

Submission Number: 2596