Large-Scale Multi-Agent Deep FBSDEs
Authors: Tianrong Chen, Ziyi O Wang, Ioannis Exarchos, Evangelos Theodorou
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We showcase superior performance of our framework over the state-of-the-art deep fictitious play algorithm on an inter-bank lending/borrowing problem in terms of multiple metrics. More importantly, our approach scales up to 3000 agents in simulation, a scale which, to the best of our knowledge, represents a new stateof-the-art. We also demonstrate the applicability of our framework in robotics on a belief space autonomous racing problem. |
| Researcher Affiliation | Academia | 1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA 2Center for Machine Learning, Georgia Institute of Technology, Atlanta, USA 3Department of Computer Science, Stanford University, Stanford, USA 4School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, USA. |
| Pseudocode | Yes | The full algorithm can be found in Appendix K. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper describes generating training data through simulation based on models (e.g., inter-bank lending/borrowing model), but it does not specify a publicly available or open dataset with access information (link, DOI, specific citation to a dataset paper). |
| Dataset Splits | No | The paper mentions 'training/test data' and 'evaluation loss' but does not provide specific percentages or counts for training, validation, or test splits. Appendix C, which is referenced for metrics on training/test data, is not provided. |
| Hardware Specification | Yes | The hardware used to run all simulations is included in Appendix J. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers (e.g., Python 3.x, PyTorch 1.x) that were used for the experiments. |
| Experiment Setup | Yes | All experiment configurations can be found in Appendix F. The hyperparameters and dynamics coefficients used in the inter-bank experiments are the same as (Han & Hu, 2019) unless otherwise noted. |