On the alleviation of the problem of local minima in back-propagation
Proceedings of second world congress on Nonlinear analysts
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
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)
Coordinated exploration in multi-agent reinforcement learning: an application to load-balancing
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
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A novel Nash equilibria (NE) learning algorithm for finite strategic games is presented in this paper. Based on an assumption that each player tries to maximize his own payoff, the algorithm explores the policies of the players in the policy profile space to increase the payoffs in each learning iteration. This paper investigates the effectiveness of the algorithm and show experimentally that the proposed algorithm accelerates the policy learning process. This algorithm learns faster than other proposed intelligent learning approaches, and can learn almost all the existing NE for a finite strategic game.