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ATAL
2007
Springer

Theoretical advantages of lenient Q-learners: an evolutionary game theoretic perspective

9 years 7 months ago
Theoretical advantages of lenient Q-learners: an evolutionary game theoretic perspective
This paper presents the dynamics of multiple reinforcement learning agents from an Evolutionary Game Theoretic (EGT) perspective. We provide a Replicator Dynamics model for traditional multiagent Q-learning, and we extend these differential equations to account for lenient learners: agents that forgive possible mistakes of their teammates that resulted in lower rewards. We use this extended formal model to visualize the basins of attraction of both traditional and lenient multiagent Q-learners in two benchmark coordination problems. The results indicate that lenience provides learners with more accurate estimates for the utility of their actions, resulting in higher likelihood of convergence to the globally optimal solution. In addition, our research supports the strength of EGT as a backbone for multiagent reinforcement learning.
Liviu Panait, Karl Tuyls
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where ATAL
Authors Liviu Panait, Karl Tuyls
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