Monte carlo sampling and regret minimization for equilibrium computation and decision-making in large extensive form games
By Michael H. Bowling, Marc Lanctot. 2013
47 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Reinforcement Learning, 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 Economics and Econometrics
- Marc Lanctot
- Noam Brown
- Neil Burch
- Tuomas Sandholm
- Karl Tuyls
- Julien Pérolat
- R´emi Munos
- Dustin Morrill
In Reinforcement Learning
- Karl Tuyls
- R´emi Munos
- Marc Lanctot
- Julien Pérolat
- Jakob N. Foerster
- Adam Lerer
- Yuxi Li
- Sergey Levine
In Game & Decision Theory
- Noam Brown
- Karl Tuyls
- Tuomas Sandholm
- Marc Lanctot
- Julien Pérolat
- Neil Burch
- Adam Lerer
- Michael H. Bowling
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”
- Neural Replicator Dynamics (2019, methodology): “We use an imperfect information variant with 4 cards where bid cards are not revealed [30].”
- Actor-Critic Policy Optimization in Partially Observable Multiagent Environments (2018, background): “For an overview with more background, see [47, Chapter 2].”
- An Exact Double-Oracle Algorithm for Zero-Sum Extensive-Form Games with Imperfect Information (2014, result): “We compare our results with state-of-the-art algorithms for finding both exact and approximate solutions: linear programming using the sequence form, and Counterfactual Regret Minimization (CFR, Zinkevich, Johanson, Bowling, & Piccione, 2008; Lanctot, 2013).”
- From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization (2020, background): “, 2019), regret minimization algorithms (Zinkevich et al., 2008; Lanctot, 2013; Lanctot et al., 2009) (which suffer from the aforementioned time-averaging problem), best response algorithms (Heinrich et al.”
- Deep Counterfactual Regret Minimization (2018, background): “, where external sampling permits δ = 1 (Lanctot, 2013).”
- An Introduction to Counterfactual Regret Minimization (2013, background): “This form of Monte Carlo style sampling is called “chance-sampling”, though it is interesting to note that CFR can be implemented without sampling at all (“Vanilla CFR”) or with many different forms of sampling schemes [9].”