Technical Note: \cal Q-Learning
Machine Learning
A selection-mutation model for q-learning in multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
The Journal of Machine Learning Research
Frequency adjusted multi-agent Q-learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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Recently, an evolutionary model of Lenient Q-learning (LQ) has been proposed, providing theoretical guarantees of convergence to the global optimum in cooperative multi-agent learning. However, experiments reveal discrepancies between the predicted dynamics of the evolutionary model and the actual learning behavior of the Lenient Q-learning algorithm, which undermines its theoretical foundation. Moreover it turns out that the predicted behavior of the model is more desirable than the observed behavior of the algorithm. We propose the variant Lenient Frequency Adjusted Qlearning (LFAQ) which inherits the theoretical guarantees and resolves this issue.