63 search results - page 1 / 13 » Convergence and No-Regret in Multiagent Learning |
NIPS
2004
13 years 5 months ago
2004
Learning in a multiagent system is a challenging problem due to two key factors. First, if other agents are simultaneously learning then the environment is no longer stationary, t...
LAMAS
2005
Springer
13 years 9 months ago
2005
Springer
We present a new multiagent learning algorithm, RVσ(t), that builds on an earlier version, ReDVaLeR . ReDVaLeR could guarantee (a) convergence to best response against stationary ...
COLT
2003
Springer
13 years 9 months ago
2003
Springer
A general class of no-regret learning algorithms, called no-Φ-regret learning algorithms, is defined which spans the spectrum from no-external-regret learning to no-internal-reg...
ICML
2003
IEEE
14 years 5 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...
ATAL
2009
Springer
13 years 11 months ago
2009
Springer
This paper introduces a multiagent reinforcement learning algorithm that converges with a given accuracy to stationary Nash equilibria in general-sum discounted stochastic games. ...