ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1802.05642
  4. Cited By
The Mechanics of n-Player Differentiable Games

The Mechanics of n-Player Differentiable Games

15 February 2018
David Balduzzi
S. Racanière
James Martens
Jakob N. Foerster
K. Tuyls
T. Graepel
    MLT
ArXivPDFHTML

Papers citing "The Mechanics of n-Player Differentiable Games"

18 / 168 papers shown
Title
Multi-Agent Learning in Network Zero-Sum Games is a Hamiltonian System
Multi-Agent Learning in Network Zero-Sum Games is a Hamiltonian System
James P. Bailey
Georgios Piliouras
19
40
0
05 Mar 2019
Training GANs with Centripetal Acceleration
Training GANs with Centripetal Acceleration
Wei Peng
Yuhong Dai
Hui Zhang
Lizhi Cheng
GAN
40
43
0
24 Feb 2019
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order
  Methods
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods
Maher Nouiehed
Maziar Sanjabi
Tianjian Huang
Jason D. Lee
Meisam Razaviyayn
48
338
0
21 Feb 2019
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
Ying Wen
Yaodong Yang
Rui Luo
Jun Wang
Wei Pan
LRM
39
148
0
26 Jan 2019
Open-ended Learning in Symmetric Zero-sum Games
Open-ended Learning in Symmetric Zero-sum Games
David Balduzzi
M. Garnelo
Yoram Bachrach
Wojciech M. Czarnecki
Julien Perolat
Max Jaderberg
T. Graepel
21
167
0
23 Jan 2019
On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in
  Zero-Sum Games
On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games
Eric V. Mazumdar
Michael I. Jordan
S. Shankar Sastry
40
121
0
03 Jan 2019
Stable Opponent Shaping in Differentiable Games
Stable Opponent Shaping in Differentiable Games
Alistair Letcher
Jakob N. Foerster
David Balduzzi
Tim Rocktaschel
Shimon Whiteson
26
109
0
20 Nov 2018
Finding Mixed Nash Equilibria of Generative Adversarial Networks
Finding Mixed Nash Equilibria of Generative Adversarial Networks
Ya-Ping Hsieh
Chen Liu
S. Chakrabartty
GAN
27
91
0
23 Oct 2018
A Survey and Critique of Multiagent Deep Reinforcement Learning
A Survey and Critique of Multiagent Deep Reinforcement Learning
Pablo Hernandez-Leal
Bilal Kartal
Matthew E. Taylor
OffRL
48
553
0
12 Oct 2018
Global Convergence to the Equilibrium of GANs using Variational
  Inequalities
Global Convergence to the Equilibrium of GANs using Variational Inequalities
I. Gemp
Sridhar Mahadevan
28
50
0
04 Aug 2018
Acceleration through Optimistic No-Regret Dynamics
Acceleration through Optimistic No-Regret Dynamics
Jun-Kun Wang
Jacob D. Abernethy
6
44
0
27 Jul 2018
Negative Momentum for Improved Game Dynamics
Negative Momentum for Improved Game Dynamics
Gauthier Gidel
Reyhane Askari Hemmat
Mohammad Pezeshki
Rémi Le Priol
Gabriel Huang
Simon Lacoste-Julien
Ioannis Mitliagkas
AI4CE
27
179
0
12 Jul 2018
Optimistic mirror descent in saddle-point problems: Going the extra
  (gradient) mile
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
P. Mertikopoulos
Bruno Lecouat
Houssam Zenati
Chuan-Sheng Foo
V. Chandrasekhar
Georgios Piliouras
43
292
0
07 Jul 2018
Modeling Friends and Foes
Modeling Friends and Foes
Pedro A. Ortega
Shane Legg
AAML
8
3
0
30 Jun 2018
The Online Saddle Point Problem and Online Convex Optimization with
  Knapsacks
The Online Saddle Point Problem and Online Convex Optimization with Knapsacks
Adrian Rivera Cardoso
He Wang
Huan Xu
32
11
0
21 Jun 2018
The Unusual Effectiveness of Averaging in GAN Training
The Unusual Effectiveness of Averaging in GAN Training
Yasin Yazici
Chuan-Sheng Foo
Stefan Winkler
Kim-Hui Yap
Georgios Piliouras
V. Chandrasekhar
27
173
0
12 Jun 2018
Re-evaluating Evaluation
Re-evaluating Evaluation
David Balduzzi
K. Tuyls
Julien Perolat
T. Graepel
MoMe
30
97
0
07 Jun 2018
First-order Methods Almost Always Avoid Saddle Points
First-order Methods Almost Always Avoid Saddle Points
Jason D. Lee
Ioannis Panageas
Georgios Piliouras
Max Simchowitz
Michael I. Jordan
Benjamin Recht
ODL
95
83
0
20 Oct 2017
Previous
1234