Empirical and theoretical support for lenient learning
暂无分享,去创建一个
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.
[1] Tom Lenaerts,et al. A selection-mutation model for q-learning in multi-agent systems , 2003, AAMAS '03.
[2] Karl Tuyls,et al. Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective , 2008, J. Mach. Learn. Res..
[3] Karl Tuyls,et al. Frequency adjusted multi-agent Q-learning , 2010, AAMAS.
[4] Peter Dayan,et al. Q-learning , 1992, Machine Learning.