24 search results - page 3 / 5 » On No-Regret Learning, Fictitious Play, and Nash Equilibrium |
NIPS
2001
13 years 6 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
13 years 3 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
14 years 6 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
13 years 6 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
14 years 6 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...