Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.00629 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929584227745792 |
|---|---|
| author | Rajpal, Mohit Tran, Lac Gia Zhang, Yehong Low, Bryan Kian Hsiang |
| author_facet | Rajpal, Mohit Tran, Lac Gia Zhang, Yehong Low, Bryan Kian Hsiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_00629 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Dependency Structure Search Bayesian Optimization for Decision Making Models Rajpal, Mohit Tran, Lac Gia Zhang, Yehong Low, Bryan Kian Hsiang Machine Learning Artificial Intelligence 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. |
| title | Dependency Structure Search Bayesian Optimization for Decision Making Models |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2308.00629 |