Using Regret Estimation to Solve Games Compactly

By Dustin Morrill. 2016

17 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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

1. Zhuoran Yang
2. Kaiqing Zhang
3. Tamer Basar
4. Sergey Levine
5. Yoshua Bengio
6. Julien Pérolat
7. Karl Tuyls
8. Marc Lanctot

In Game & Decision Theory

1. Zhuoran Yang
2. Karl Tuyls
3. Julien Pérolat
4. Tamer Basar
5. Kaiqing Zhang
6. Jean-Baptiste Lespiau
7. Shayegan Omidshafiei
8. Marc Lanctot

Citations in the corpus

• OpenSpiel: A Framework for Reinforcement Learning in Games (2019, methodology): “Regression CFR Regression CFR (RCFR) was the first variant to combine RL-style function approximation with CFR techniques [73, 46].”
• Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms (2019, methodology): “For example, [281, 282, 283, 284, 285, 286] combine CFR with Monte-Carlo sampling; [287, 288, 289] propose to estimate the counterfactual value functions via regression; [290, 291, 292] improve the computational efficiency by pruning suboptimal paths in the game tree; [293, 294, 295] analyze the per”