Scalable Learning for Spatiotemporal Mean Field Games Using ...

This paper proposes a scalable learning framework to solve a system of coupled forward–backward partial differential equations (PDEs) arising from mean ...

A Hybrid Framework of Reinforcement Learning and Physics-Informed Deep Learning for Spatiotemporal M... ... Mean field games (MFG) are developed ...

Scalable Learning for Spatiotemporal Mean Field Games Using

A learning algorithm is devised and its performance is evaluated on one application domain, which is the autonomous driving velocity control. Numerical ...

Scalable Learning for Spatiotemporal Mean Field Games ... - CiteDrive

Scalable Learning for Spatiotemporal Mean Field Games Using Physics-Informed Neural Operator ... This paper proposes a scalable learning framework to solve a ...

Scalable Learning for Spatiotemporal Mean Field Games ... - OUCI

This paper proposes a scalable learning framework to solve a system of coupled forward–backward partial differential equations (PDEs) arising from mean ...

LovelyBuggies/MFG-PINO - GitHub

Code for the paper: Scalable Learning for Spatiotemporal Mean-Field Games using Physics-Informed Neural Operator.

Scalable Deep Reinforcement Learning Algorithms for Mean Field ...

Mean Field Games (MFGs) have been intro- duced to efficiently approximate games with very large populations of strategic agents. Recently,.

Scalable Offline Reinforcement Learning for Mean Field Games - arXiv

Reinforcement learning algorithms for mean-field games offer a scalable framework for optimizing policies in large populations of interacting agents. Existing ...

Informed Deep Learning for Spatiotemporal Mean Field Games

The other is a pure PIDL framework that updates agents' states and population density altogether using deep neu- ral networks. Both the proposed ...

Mathematics | Free Full-Text | Scalable Learning for Spatiotemporal ...

Scalable Learning for Spatiotemporal Mean Field Games Using Physics-Informed Neural Operator. Mathematics 2024, 12, 803. https://doi.org/10.3390 ...

Scalable Offline Reinforcement Learning for Mean Field Games - arXiv

Current MFG methods have largely overlooked the offline setting, where no online interaction with the environment is available during learning.

Physics-Informed Neural Operator for Coupled Forward-Backward ...

forcement learning and physics-informed deep learning for spatiotemporal mean field games. ... Scalable deep reinforcement learning algo- rithms for mean field ...

Scalable learning framework for an ST-MFG. - ResearchGate

Download scientific diagram | Scalable learning framework for an ST-MFG. from publication: Scalable Learning for Spatiotemporal Mean Field Games Using ...

An online interactive physics-informed adversarial network for ...

Numerical experiments validate the effectiveness of IPIAN in solving high-dimensional mean field game models, as demonstrated by obstacle avoidance experiments ...

[PDF] Scaling up Mean Field Games with Online Mirror Descent

This study establishes the state-of-the-art for learning in large-scale multi-agent and multi-population games by showing that continuous-time OMD provably ...

OUCI

Scalable Learning for Spatiotemporal Mean Field Games Using Physics-Informed Neural Operator. Shuo Liu, Xu Chen, Xuan Di. This paper proposes a scalable ...

Reinforcement Learning for Finite Space Mean-Field Type Games

Although the theory has been extensively developed, we are still lacking efficient and scalable computational methods. Here, we develop ...

Physics-Informed Graph Neural Operator for Mean Field Games on ...

Our contributions include: (1) We propose a scalable learning framework leveraging. PIGNO to solve G-MFGs with various initial population states ...

N-Player Games and Mean Field Games of Moderate - ProQuest

Scalable Learning for Spatiotemporal Mean Field Games Using Physics-Informed Neural Operator · A Stochastic Discrete Fractional Cournot Duopoly Game: Modeling, ...

A Hybrid Framework of Reinforcement Learning and Physics ...

Mean field games (MFG) are developed to solve equilibria in multi-agent systems (MAS) with many agents. The majority of literature on MFGs ...