Mean|Field Multi|Agent Reinforcement Learning

[1802.05438] Mean Field Multi-Agent Reinforcement Learning - arXiv

In this paper, we present \emph{Mean Field Reinforcement Learning} where the interactions within the population of agents are approximated by ...

Mean Field Multi-Agent Reinforcement Learning

ported for many agent systems. Page 2. Mean Field Multi-Agent Reinforcement Learning. 2. Preliminary. MARL intersects between reinforcement learning and game.

Mean Field Multi-Agent Reinforcement Learning

Mean Field Multi-Agent Reinforcement LearningYaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, Jun WangExisting multi-agent ...

Major-Minor Mean Field Multi-Agent Reinforcement Learning - arXiv

Multi-agent reinforcement learning (MARL) remains difficult to scale to many agents. Recent MARL using Mean Field Control (MFC) provides a ...

mlii/mfrl: Mean Field Multi-Agent Reinforcement Learning - GitHub

Mean Field Multi-Agent Reinforcement Learning. Contribute to mlii/mfrl development by creating an account on GitHub.

Efficient Model-Based Multi-Agent Mean-Field Reinforcement ...

Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC, obtained via a novel mean-field type ...

Adaptive mean field multi-agent reinforcement learning

We propose adaptive mean field MARL, which is based on the attention mechanism and can be used to deal with many-agent scenarios.

Provably Efficient Offline Mean-Field Multi-Agent RL - NIPS

Mean-Field Multi-Agent Reinforcement Learning (MF-MARL) is attractive in the applications involving a large population of homogeneous agents, as it exploits.

Causal Mean Field Multi-Agent Reinforcement Learning - IEEE Xplore

Scalability remains a challenge in multi-agent reinforcement learning and is currently under active research. A framework named mean-field reinforcement ...

Hierarchical Mean-Field Deep Reinforcement Learning for Large ...

Mean-Field (MF)-based methods address this issue by transforming the interactions within the whole system into a single agent played with the ...

Xin Guo: Mean-field multi-agent reinforcement learning - YouTube

Atelier/Workshop: Jeux à champ moyen/Mean Field Games 15 Avril/April 15: http://www.crm.umontreal.ca/2022/Games22/horaire_e.html Xin Guo: ...

Foundations of Multi-Agent and Mean Field Reinforcement Learning

This event is part of Theoretical Advances in Reinforcement Learning and Control View Details. Foundations of Multi-Agent and Mean Field Reinforcement Learning.

Multi Type Mean Field Reinforcement Learning - RBC Borealis

... field of many agent reinforcement learning, based on the standard MAgents framework. ... Bibtex. @inproceedings{MTMFRL2020, title={Multi Type Mean Field ...

Mean-Field Multi-Agent Reinforcement Learning for Peer-to-Peer ...

A novel multi-agent reinforcement learning method that allows each prosumer agent to stabilize the training performance with mean-field approximation.

GAT-MF: Graph Attention Mean Field for Very Large Scale Multi ...

GAT-MF: Graph Attention Mean Field for Very Large Scale Multi-Agent Reinforcement Learning · Abstract · Supplementary Material · References · Cited ...

Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning

Mean-field reinforcement learning ad- dresses the resulting scalability challenge by optimizing the policy of a representative agent interacting ...

Efficient Model-Based Multi-Agent Mean-Field Reinforcement ...

(2022; 2021) extend the Q-Learning algorithm and show convergence for specific variants of MFC focusing on discrete state and action spaces. The mean-field ...

Mean Field Multi-Agent Reinforcement Learning Method for Area ...

A traffic signal control model based on Mean Field Multi-Agent Reinforcement Learning (MFMARL) was constructed, containing two algorithms: Mean Field Q-Network ...

Mean-Field Multiagent Reinforcement Learning: A Decentralized ...

Mean-Field MARL with Local Dependency. The focus of this paper is to study a cooperative multiagent system with a network of agent states, which ...

Multi-agent reinforcement learning - Wikipedia

Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that ...