24 search results - page 3 / 5 » On No-Regret Learning, Fictitious Play, and Nash Equilibrium |
NIPS
2001
14 years 11 months ago
2001
We propose a new classification for multi-agent learning algorithms, with each league of players characterized by both their possible strategies and possible beliefs. Using this c...
SAGT
2010
Springer
14 years 8 months ago
2010
Springer
Can learning algorithms find a Nash equilibrium? This is a natural question for several reasons. Learning algorithms resemble the behavior of players in many naturally arising gam...
ICML
2003
IEEE
15 years 11 months ago
2003
IEEE
Learning in many multi-agent settings is inherently repeated play. This calls into question the naive application of single play Nash equilibria in multi-agent learning and sugges...
NIPS
2003
14 years 11 months ago
2003
We study how to learn to play a Pareto-optimal strict Nash equilibrium when there exist multiple equilibria and agents may have different preferences among the equilibria. We focu...
ICML
2003
IEEE
15 years 11 months ago
2003
IEEE
A satisfactory multiagent learning algorithm should, at a minimum, learn to play optimally against stationary opponents and converge to a Nash equilibrium in self-play. The algori...