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Neural Replicator Dynamics : Multiagent Learning via Hed..:

, In: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems,

Hennes, Daniel ; Morrill, Dustin ; Omidshafiei, Shayegan... - p. 492-501 , 2020

Link: https://dl.acm.org/doi/10.5555/3398761.3398822

RT 
T1 <cTitle>Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems</cTitle>: T1 <Title>Neural Replicator Dynamics				: Multiagent Learning via Hedging Policy Gradients</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3398822&Exemplar=1&LAN=DE
				
A1 Hennes, Daniel
A1 Morrill, Dustin
A1 Omidshafiei, Shayegan
A1 Munos, Rémi
A1 Perolat, Julien
A1 Lanctot, Marc
A1 Gruslys, Audrunas
A1 Lespiau, Jean-Baptiste
A1 Parmas, Paavo
A1 Duèñez-Guzmán, Edgar
A1 Tuyls, Karl
PB International Foundation for Autonomous Agents and Multiagent Systems
YR 2020
K1 games
K1 multiagent
K1 regret minimization
K1 reinforcement learning
K1 Theory of computation
K1 Theory and algorithms for application domains
K1 Machine learning theory
K1 Online learning theory
K1 Multi-agent learning
K1 Computing methodologies
K1 Machine learning
K1 Learning paradigms
K1 Reinforcement learning
K1 Multi-agent reinforcement learning
K1 Algorithmic game theory and mechanism design
K1 Algorithmic game theory
K1 Regret bounds
SP 492
OP 501
LK http://dx.doi.org/https://dl.acm.org/doi/10.5555/3398761.3398822
DO https://dl.acm.org/doi/10.5555/3398761.3398822
SF ELIB - SuUB Bremen

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