A Fictitious Play Approach to Large-Scale Optimization
By Theodore J. Lambert, Marina A. Epelman, Robert L. Smith. 2005
102 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research
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
- Marc Lanctot
- David Silver
- Johannes Heinrich
- Jason R. Marden
- Jeff S. Shamma
- Sergey Levine
- Yuxi Li
- Pieter Abbeel
In Management Science and Operations Research
- Jason R. Marden
- David Silver
- Johannes Heinrich
- Jeff S. Shamma
- Gürdal Arslan
- Marc Lanctot
- Sergey Levine
- Karl Henrik Johansson
Citations in the corpus
- Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (2016, background): “…studying recreational games is to develop algorithms that will scale to more complex, real-world games such as airport and network security, financial and energy trading, traffic control and routing (Lambert III et al., 2005; Nevmyvaka et al., 2006; Bazzan, 2009; Tambe, 2011; Urieli and Stone, 2014).”
- Fictitious Self-Play in Extensive-Form Games (2015, background): “Despite the popularity of fictitious play to date, it has seen use in few large-scale applications, e.g. (Lambert III et al., 2005; McMahan & Gordon, 2007; Ganzfried & Sandholm, 2009; Heinrich & Silver, 2015).”
- Autonomous Vehicle-Target Assignment: A Game-Theoretical Formulation (2007, background): “The most computationally intensive operation is the optimization of the utilities during the negotiations, which effectively requires an enumeration of all possible combined assignments by other vehicles (cid:1) 21,22 (cid:2) .”