TY - GEN
T1 - RVσ(t)
T2 - Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
AU - Banerjee, Bikramjit
AU - Peng, Jing
PY - 2006
Y1 - 2006
N2 - We present a new multiagent learning algorithm (RVσ(t)) that can guarantee both no-regret performance (all games) and policy convergence (some games of arbitrary size). Unlike its predecessor ReDVaLeR, it (1) does not need to distinguish whether its opponents are self-play or otherwise non-stationary, (2) is allowed to know its portion of any equilibrium that, we argue, leads to convergence in some games in addition to no-regret. Although the regret of RVσ(t) is analyzed in continuous time, we show that it grows slower than in other no-regret techniques like GIGA and GIGA-WoLF. We show that RVσ(t) can converge to coordinated behavior in coordination games, while GIGA, GIGA-WoLF may converge to poorly coordinated (mixed) behaviors.
AB - We present a new multiagent learning algorithm (RVσ(t)) that can guarantee both no-regret performance (all games) and policy convergence (some games of arbitrary size). Unlike its predecessor ReDVaLeR, it (1) does not need to distinguish whether its opponents are self-play or otherwise non-stationary, (2) is allowed to know its portion of any equilibrium that, we argue, leads to convergence in some games in addition to no-regret. Although the regret of RVσ(t) is analyzed in continuous time, we show that it grows slower than in other no-regret techniques like GIGA and GIGA-WoLF. We show that RVσ(t) can converge to coordinated behavior in coordination games, while GIGA, GIGA-WoLF may converge to poorly coordinated (mixed) behaviors.
KW - Game theory
KW - Multiagent learning
UR - http://www.scopus.com/inward/record.url?scp=34247189601&partnerID=8YFLogxK
U2 - 10.1145/1160633.1160775
DO - 10.1145/1160633.1160775
M3 - Conference contribution
AN - SCOPUS:34247189601
SN - 1595933034
SN - 9781595933034
T3 - Proceedings of the International Conference on Autonomous Agents
SP - 798
EP - 800
BT - Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems
Y2 - 8 May 2006 through 12 May 2006
ER -