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Many approaches for optimizing decision making models rely on gradient basedmethods requiring informative feedback from the environment. However, in thecase where such feedback is sparse or uninformative, such approaches may resultin poor performance. Derivative-free approaches such as Bayesian Optimizationmitigate the dependency on the quality of gradient feedback, but are known toscale poorly in the high-dimension setting of complex decision making models.This problem is exacerbated if the model requires interactions between severalagents cooperating to accomplish a shared goal. To address the dimensionalitychallenge, we propose a compact multi-layered architecture modeling thedynamics of agent interactions through the concept of role. We introduceDependency Structure Search Bayesian Optimization to efficiently optimize themulti-layered architecture parameterized by a large number of parameters, andshow an improved regret bound. Our approach shows strong empirical resultsunder malformed or sparse reward.

Dependency Structure Search Bayesian Optimization for Decision Making Models