63 search results - page 1 / 13 » Convergence and No-Regret in Multiagent Learning |
NIPS
2004
13 years 6 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 10 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 10 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 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...
ATAL
2009
Springer
13 years 12 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. ...