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Independent Natural Policy Gradient Methods for Potential Games:
  Finite-time Global Convergence with Entropy Regularization
v1v2 (latest)

Independent Natural Policy Gradient Methods for Potential Games: Finite-time Global Convergence with Entropy Regularization

12 April 2022
Shicong Cen
Fan Chen
Yuejie Chi
ArXiv (abs)PDFHTML

Papers citing "Independent Natural Policy Gradient Methods for Potential Games: Finite-time Global Convergence with Entropy Regularization"

26 / 26 papers shown
Title
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural
  Policy Gradient Methods
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods
Yanli Liu
Kai Zhang
Tamer Basar
W. Yin
111
110
0
15 Nov 2022
Independent Policy Gradient for Large-Scale Markov Potential Games:
  Sharper Rates, Function Approximation, and Game-Agnostic Convergence
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence
Dongsheng Ding
Chen-Yu Wei
Kai Zhang
M. Jovanović
76
69
0
08 Feb 2022
Near-Optimal No-Regret Learning for Correlated Equilibria in
  Multi-Player General-Sum Games
Near-Optimal No-Regret Learning for Correlated Equilibria in Multi-Player General-Sum Games
Ioannis Anagnostides
C. Daskalakis
Gabriele Farina
Maxwell Fishelson
Noah Golowich
Tuomas Sandholm
146
56
0
11 Nov 2021
V-Learning -- A Simple, Efficient, Decentralized Algorithm for
  Multiagent RL
V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL
Chi Jin
Qinghua Liu
Yuanhao Wang
Tiancheng Yu
OffRL
78
132
0
27 Oct 2021
Independent Natural Policy Gradient Always Converges in Markov Potential
  Games
Independent Natural Policy Gradient Always Converges in Markov Potential Games
Roy Fox
Stephen Marcus McAleer
W. Overman
Ioannis Panageas
82
49
0
20 Oct 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
Kai Zhang
Tamer Bacsar
82
57
0
12 Oct 2021
Provably Efficient Reinforcement Learning in Decentralized General-Sum
  Markov Games
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games
Weichao Mao
Tamer Basar
97
67
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
111
68
0
08 Oct 2021
Near-Optimal No-Regret Learning in General Games
Near-Optimal No-Regret Learning in General Games
C. Daskalakis
Maxwell Fishelson
Noah Golowich
78
107
0
16 Aug 2021
Global Convergence of Multi-Agent Policy Gradient in Markov Potential
  Games
Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games
Stefanos Leonardos
W. Overman
Ioannis Panageas
Georgios Piliouras
85
123
0
03 Jun 2021
Gradient play in stochastic games: stationary points, convergence, and
  sample complexity
Gradient play in stochastic games: stationary points, convergence, and sample complexity
Runyu Zhang
Tongzheng Ren
Na Li
85
44
0
01 Jun 2021
Fast Policy Extragradient Methods for Competitive Games with Entropy
  Regularization
Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization
Shicong Cen
Yuting Wei
Yuejie Chi
106
79
0
31 May 2021
Policy Mirror Descent for Regularized Reinforcement Learning: A
  Generalized Framework with Linear Convergence
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence
Wenhao Zhan
Shicong Cen
Baihe Huang
Yuxin Chen
Jason D. Lee
Yuejie Chi
82
78
0
24 May 2021
Softmax Policy Gradient Methods Can Take Exponential Time to Converge
Softmax Policy Gradient Methods Can Take Exponential Time to Converge
Gen Li
Yuting Wei
Yuejie Chi
Yuxin Chen
100
53
0
22 Feb 2021
Last-iterate Convergence of Decentralized Optimistic Gradient
  Descent/Ascent in Infinite-horizon Competitive Markov Games
Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games
Chen-Yu Wei
Chung-Wei Lee
Mengxiao Zhang
Haipeng Luo
95
83
0
08 Feb 2021
Policy Mirror Descent for Reinforcement Learning: Linear Convergence,
  New Sampling Complexity, and Generalized Problem Classes
Policy Mirror Descent for Reinforcement Learning: Linear Convergence, New Sampling Complexity, and Generalized Problem Classes
Guanghui Lan
205
143
0
30 Jan 2021
Independent Policy Gradient Methods for Competitive Reinforcement
  Learning
Independent Policy Gradient Methods for Competitive Reinforcement Learning
C. Daskalakis
Dylan J. Foster
Noah Golowich
238
163
0
11 Jan 2021
On the Global Convergence Rates of Softmax Policy Gradient Methods
On the Global Convergence Rates of Softmax Policy Gradient Methods
Jincheng Mei
Chenjun Xiao
Csaba Szepesvári
Dale Schuurmans
152
294
0
13 May 2020
Neural Policy Gradient Methods: Global Optimality and Rates of
  Convergence
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence
Lingxiao Wang
Qi Cai
Zhuoran Yang
Zhaoran Wang
113
242
0
29 Aug 2019
On the Theory of Policy Gradient Methods: Optimality, Approximation, and
  Distribution Shift
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift
Alekh Agarwal
Sham Kakade
Jason D. Lee
G. Mahajan
79
321
0
01 Aug 2019
Global Optimality Guarantees For Policy Gradient Methods
Global Optimality Guarantees For Policy Gradient Methods
Jalaj Bhandari
Daniel Russo
93
193
0
05 Jun 2019
Global Convergence of Policy Gradient Methods for the Linear Quadratic
  Regulator
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
Maryam Fazel
Rong Ge
Sham Kakade
M. Mesbahi
102
611
0
15 Jan 2018
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement
  Learning with a Stochastic Actor
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Tuomas Haarnoja
Aurick Zhou
Pieter Abbeel
Sergey Levine
319
8,432
0
04 Jan 2018
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Ryan J. Lowe
Yi Wu
Aviv Tamar
J. Harb
Pieter Abbeel
Igor Mordatch
164
4,520
0
07 Jun 2017
Decentralized Q-Learning for Stochastic Teams and Games
Decentralized Q-Learning for Stochastic Teams and Games
Gürdal Arslan
S. Yüksel
72
114
0
25 Jun 2015
End-to-End Training of Deep Visuomotor Policies
End-to-End Training of Deep Visuomotor Policies
Sergey Levine
Chelsea Finn
Trevor Darrell
Pieter Abbeel
BDL
317
3,445
0
02 Apr 2015
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