Mutation-Bias Learning in Games

• URL: http://arxiv.org/abs/2405.18190v1
• Date: Tue, 28 May 2024 14:02:44 GMT
• Title: Mutation-Bias Learning in Games
• Authors: Johann Bauer, Sheldon West, Eduardo Alonso, Mark Broom,
• Abstract summary: We present two variants of a multi-agent reinforcement learning algorithm based on evolutionary game theoretic considerations. One variant enables us to prove results on its relationship to a system of ordinary differential equations of replicator-mutator dynamics type. The more complicated variant enables comparisons to Q-learning based algorithms.
• Score: 1.743685428161914
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present two variants of a multi-agent reinforcement learning algorithm based on evolutionary game theoretic considerations. The intentional simplicity of one variant enables us to prove results on its relationship to a system of ordinary differential equations of replicator-mutator dynamics type, allowing us to present proofs on the algorithm's convergence conditions in various settings via its ODE counterpart. The more complicated variant enables comparisons to Q-learning based algorithms. We compare both variants experimentally to WoLF-PHC and frequency-adjusted Q-learning on a range of settings, illustrating cases of increasing dimensionality where our variants preserve convergence in contrast to more complicated algorithms. The availability of analytic results provides a degree of transferability of results as compared to purely empirical case studies, illustrating the general utility of a dynamical systems perspective on multi-agent reinforcement learning when addressing questions of convergence and reliable generalisation.

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