Learning automata: an introduction
Learning automata: an introduction
Technical Note: \cal Q-Learning
Machine Learning
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
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Resource allocation games with changing resource capacities
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
On the use of memory and resources in minority games
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Learning in the time-dependent minority game
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
We study how a group of adaptive agents can coordinate when competing for limited resources. A popular game theoretic model for this is the Minority Game. In this article we show that the coordination among learning agents can improve when agents use different learning parameters or even evolve their learning parameters. Better coordination leads to less resources being wasted and agents achieving higher individual performance. We also show that learning algorithms which achieve good results when all agents use that same algorithm, may be outcompeted when directly confronting other learning algorithms in the Minority Game.