ArticleProceedings of the National Academy of Sciences of the United States of America2021
Alternating the population and control neural networks to solve high-dimensional stochastic mean-field games.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 6 citations in OpenAlex.
- In-context operator learning with data prompts for differential equation problems.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Explainable AI via learning to optimize.Scientific reports · 2023Article
- Decentralized Stochastic Control with Finite-Dimensional Memories: A Memory Limitation Approach.Entropy (Basel, Switzerland) · 2023Article
- Taming hyperparameter tuning in continuous normalizing flows using the JKO scheme.Scientific reports · 2023Article
- Forward-Backward Sweep Method for the System of HJB-FP Equations in Memory-Limited Partially Observable Stochastic Control.Entropy (Basel, Switzerland) · 2023Article
- Memory-Limited Partially Observable Stochastic Control and Its Mean-Field Control Approach.Entropy (Basel, Switzerland) · 2022Article
- A Mean-Field Game Control for Large-Scale Swarm Formation Flight in Dense Environments.Sensors (Basel, Switzerland) · 2022Article
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5 authors at 2 institutions in 1 country.
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Abstract
We present APAC-Net, an alternating population and agent control neural network for solving stochastic mean-field games (MFGs). Our algorithm is geared toward high-dimensional instances of MFGs that are not approachable with existing solution methods. We achieve this in two steps. First, we take advantage of the underlying variational primal-dual structure that MFGs exhibit and phrase it as a convex-concave saddle-point problem. Second, we parameterize the value and density functions by two neural networks, respectively. By phrasing the problem in this manner, solving the MFG can be interpreted as a special case of training a generative adversarial network (GAN). We show the potential of our method on up to 100-dimensional MFG problems.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.