The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Swarm intelligence
Multiagent learning using a variable learning rate
Artificial Intelligence
Learning to Reach the Pareto Optimal Nash Equilibrium as a Team
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Nash Convergence of Gradient Dynamics in General-Sum Games
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Computing Nash equilibria through computational intelligence methods
Journal of Computational and Applied Mathematics - Special issue: Selected papers of the international conference on computational methods in sciences and engineering (ICCMSE-2003)
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Learning a Nash equilibrium of a game is challengeable, and the issue of learning all the Nash equilibria seems intractable. This paper investigates the effectiveness of a momentum-based approach to learning the Nash equilibria of a game. Experimental results show the proposed algorithm can learn a Nash equilibrium in each learning iteration for a normal form strategic game. By employing a deflection technique, it can learn almost all the existing Nash equilibria of a game.