Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Resource allocation games with changing resource capacities
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Resource Allocation in the Grid Using Reinforcement Learning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Algorithmic Game Theory
Learning in minority games with multiple resources
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Heterogeneous populations of learning agents in the minority game
ALA'11 Proceedings of the 11th international conference on Adaptive and Learning Agents
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We study learning in the time-dependent Minority Game (MG). The MG is a repeated conflicting interest game involving a large number of agents. So far, the learning mechanisms studied were rather naive and involved only exploitation of the best strategy so far at the expense of exploring new strategies. Instead, we use a reinforcement learning method called Q-learning and show how it improves the results on MG extensions of increasing difficulty.