Algorithms for computing strategies in two-player simultaneous move games
By Branislav Bosanský, Viliam Lisý, Marc Lanctot, Jiri Cermak, Mark H. M. Winands. 2016
51 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Economics and Econometrics, Game & Decision Theory
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Pieter Abbeel
- Yoshua Bengio
- Yuxi Li
- R´emi Munos
- Karl Tuyls
- Ruslan Salakhutdinov
- David Silver
In Economics and Econometrics
- R´emi Munos
- Marc Lanctot
- David Silver
- Karl Tuyls
- Julien Pérolat
- Jean-Baptiste Lespiau
- Dustin Morrill
- Shayegan Omidshafiei
In Game & Decision Theory
- Karl Tuyls
- Julien Pérolat
- Marc Lanctot
- David Silver
- Jean-Baptiste Lespiau
- R´emi Munos
- Dustin Morrill
- Thore Graepel
Citations in the corpus
- OpenSpiel: A Framework for Reinforcement Learning in Games (2019, background): “Game Reference(s) Status Backgammon Wikipedia Breakthrough Wikipedia Bridge bidding Wikipedia Coin Game [56] ∼ Connect Four Wikipedia Cooperative Box-Pushing [62] ∼ Chess Wikipedia First-price Sealed-bid Auction Wikipedia Go Wikipedia Goofspiel Wikipedia Havannah Wikipedia Hex Wikipedia ∼ Kuhn poker”
- A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning (2017, background): “Extensions to the extensive-form games have been developed [67, 9, 10] but still large state spaces are problematic due to the curse of dimensionality.”
- Actor-Critic Policy Optimization in Partially Observable Multiagent Environments (2018, background): “, matching pennies (MP), rock-paperscissors (RPS), and a skewed version of the latter, called bias rock-paper-scissors (bRPS) from [10].”