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Deep Reinforcement Learning from Self-Play in Imperfect-Information
  Games
v1v2 (latest)

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

3 March 2016
Johannes Heinrich
David Silver
    SSL
ArXiv (abs)PDFHTML

Papers citing "Deep Reinforcement Learning from Self-Play in Imperfect-Information Games"

15 / 15 papers shown
Title
Learning Strategy Representation for Imitation Learning in Multi-Agent Games
Learning Strategy Representation for Imitation Learning in Multi-Agent Games
Shiqi Lei
Kanghon Lee
Linjing Li
Jinkyoo Park
OffRL
94
0
0
17 Feb 2025
Beyond Interpolation: Extrapolative Reasoning with Reinforcement Learning and Graph Neural Networks
Beyond Interpolation: Extrapolative Reasoning with Reinforcement Learning and Graph Neural Networks
Niccolò Grillo
Andrea Toccaceli
Joël Mathys
Benjamin Estermann
Stefania Fresca
Roger Wattenhofer
AI4CELRM
281
0
0
06 Feb 2025
Transformer Guided Coevolution: Improved Team Selection in Multiagent Adversarial Team Games
Transformer Guided Coevolution: Improved Team Selection in Multiagent Adversarial Team Games
Pranav Rajbhandari
Prithviraj Dasgupta
D. Sofge
88
0
0
17 Oct 2024
A Survey on Self-play Methods in Reinforcement Learning
A Survey on Self-play Methods in Reinforcement Learning
Chao Yu
Zelai Xu
Chengdong Ma
Chao Yu
Weijuan Tu
...
Deheng Ye
Wenbo Ding
Yaodong Yang
Yu Wang
Yu Wang
SyDaSSLOnRL
139
9
0
02 Aug 2024
Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game
Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game
Zelai Xu
Chao Yu
Fei Fang
Yu Wang
Yi Wu
LLMAG
119
94
0
29 Oct 2023
Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field
  Control/Game in Continuous Time
Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time
Weichen Wang
Jiequn Han
Zhuoran Yang
Zhaoran Wang
87
29
0
16 Aug 2020
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
Matej Moravcík
Martin Schmid
Neil Burch
Viliam Lisý
Dustin Morrill
Nolan Bard
Trevor Davis
Kevin Waugh
Michael Bradley Johanson
Michael Bowling
BDL
218
913
0
06 Jan 2017
The Predictron: End-To-End Learning and Planning
The Predictron: End-To-End Learning and Planning
David Silver
H. V. Hasselt
Matteo Hessel
Tom Schaul
A. Guez
...
Gabriel Dulac-Arnold
David P. Reichert
Neil C. Rabinowitz
André Barreto
T. Degris
79
291
0
28 Dec 2016
Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in
  Poker Games
Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games
Nikolai Yakovenko
Liangliang Cao
Colin Raffel
James Fan
SSL
71
30
0
22 Sep 2015
Continuous control with deep reinforcement learning
Continuous control with deep reinforcement learning
Timothy Lillicrap
Jonathan J. Hunt
Alexander Pritzel
N. Heess
Tom Erez
Yuval Tassa
David Silver
Daan Wierstra
330
13,295
0
09 Sep 2015
Monte Carlo Planning method estimates planning horizons during
  interactive social exchange
Monte Carlo Planning method estimates planning horizons during interactive social exchange
A. Hula
P. Montague
Peter Dayan
114
99
0
12 Feb 2015
Move Evaluation in Go Using Deep Convolutional Neural Networks
Move Evaluation in Go Using Deep Convolutional Neural Networks
Chris J. Maddison
Aja Huang
Ilya Sutskever
David Silver
FAtt
105
134
0
20 Dec 2014
Solving Games with Functional Regret Estimation
Solving Games with Functional Regret Estimation
Kevin Waugh
Dustin Morrill
J. Andrew Bagnell
Michael Bowling
OffRL
87
58
0
28 Nov 2014
Deep Learning in Neural Networks: An Overview
Deep Learning in Neural Networks: An Overview
Jürgen Schmidhuber
HAI
250
16,405
0
30 Apr 2014
Bayes' Bluff: Opponent Modelling in Poker
Bayes' Bluff: Opponent Modelling in Poker
F. Southey
Michael Bowling
Bryce Larson
Carmelo Piccione
Neil Burch
Darse Billings
D. C. Rayner
178
263
0
04 Jul 2012
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