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Learning Parametric Closed-Loop Policies for Markov Potential Games

Learning Parametric Closed-Loop Policies for Markov Potential Games

3 February 2018
Sergio Valcarcel Macua
Javier Zazo
S. Zazo
ArXivPDFHTML

Papers citing "Learning Parametric Closed-Loop Policies for Markov Potential Games"

13 / 13 papers shown
Title
Beyond Theorems: A Counterexample to Potential Markov Game Criteria
Beyond Theorems: A Counterexample to Potential Markov Game Criteria
Fatemeh Fardno
S. Zahedi
52
0
0
13 May 2024
MANSA: Learning Fast and Slow in Multi-Agent Systems
MANSA: Learning Fast and Slow in Multi-Agent Systems
D. Mguni
Hao Chen
Taher Jafferjee
Jianhong Wang
Long Fei
Xidong Feng
Stephen Marcus McAleer
Feifei Tong
Jun Wang
Yaodong Yang
30
1
0
12 Feb 2023
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games
Fivos Kalogiannis
Ioannis Anagnostides
Ioannis Panageas
Emmanouil-Vasileios Vlatakis-Gkaragkounis
Vaggos Chatziafratis
S. Stavroulakis
46
13
0
03 Aug 2022
Convergence and Price of Anarchy Guarantees of the Softmax Policy
  Gradient in Markov Potential Games
Convergence and Price of Anarchy Guarantees of the Softmax Policy Gradient in Markov Potential Games
Dingyang Chen
Qi Zhang
Thinh T. Doan
29
12
0
15 Jun 2022
Learning in Congestion Games with Bandit Feedback
Learning in Congestion Games with Bandit Feedback
Qiwen Cui
Zhihan Xiong
Maryam Fazel
S. Du
26
12
0
04 Jun 2022
Learning Distributed and Fair Policies for Network Load Balancing as
  Markov Potential Game
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game
Zhiyuan Yao
Zihan Ding
OffRL
24
2
0
03 Jun 2022
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent
  Learning
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning
D. Mguni
Taher Jafferjee
Jianhong Wang
Oliver Slumbers
Nicolas Perez Nieves
Feifei Tong
Yang Li
Jiangcheng Zhu
Yaodong Yang
Jun Wang
45
18
0
05 Dec 2021
On Improving Model-Free Algorithms for Decentralized Multi-Agent
  Reinforcement Learning
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning
Weichao Mao
Lin F. Yang
Kaipeng Zhang
Tamer Bacsar
43
57
0
12 Oct 2021
When Can We Learn General-Sum Markov Games with a Large Number of
  Players Sample-Efficiently?
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?
Ziang Song
Song Mei
Yu Bai
74
67
0
08 Oct 2021
Learning in Nonzero-Sum Stochastic Games with Potentials
Learning in Nonzero-Sum Stochastic Games with Potentials
D. Mguni
Yutong Wu
Yali Du
Yaodong Yang
Ziyi Wang
Minne Li
Ying Wen
Joel Jennings
Jun Wang
32
45
0
16 Mar 2021
Stochastic Potential Games
David Mguni
9
7
0
27 May 2020
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and
  Algorithms
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms
Kaipeng Zhang
Zhuoran Yang
Tamer Basar
63
1,184
0
24 Nov 2019
Coordinating the Crowd: Inducing Desirable Equilibria in Non-Cooperative
  Systems
Coordinating the Crowd: Inducing Desirable Equilibria in Non-Cooperative Systems
D. Mguni
Joel Jennings
Sergio Valcarcel Macua
Emilio Sison
S. Ceppi
Enrique Munoz de Cote
14
39
0
30 Jan 2019
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