Network bottlenecks and task structure control the evolution of interpretable learning rules in a foraging agent

Emmanouil Giannakakis, Sina Khajehabdollahi, Anna Levina
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Abstract

Developing reliable mechanisms for continuous local learning is a central challenge faced by biological and artificial systems. Yet, how the environmental factors and structural constraints on the learning network influence the optimal plasticity mechanisms remains obscure even for simple settings. To elucidate these dependencies, we study meta-learning via evolutionary optimization of simple reward-modulated plasticity rules in embodied agents solving a foraging task. We show that unconstrained meta-learning leads to the emergence of diverse plasticity rules. However, regularization and bottlenecks to the model help reduce this variability, resulting in interpretable rules. Our findings indicate that the meta-learning of plasticity rules is very sensitive to various parameters, with this sensitivity possibly reflected in the learning rules found in biological networks. When included in models, these dependencies can be used to discover potential objective functions and details of biological learning via comparisons with experimental observations.
网络瓶颈和任务结构控制着觅食机器人可解释学习规则的演化
开发可靠的持续局部学习机制是生物和人工系统面临的核心挑战。然而,环境因素和学习网络的结构限制如何影响最佳可塑性机制,即使是在简单的环境中,也仍然是模糊不清的。为了阐明这些依赖关系,我们研究了在解决觅食任务的本体中通过进化优化简单奖励调制可塑性规则的元学习。我们的研究表明,无约束元学习会导致多种可塑性规则的出现。然而,模型的正则化和瓶颈有助于减少这种可变性,从而产生可解释的规则。我们的研究结果表明,可塑性规则的元学习对各种参数非常敏感,这种敏感性可能反映在生物网络的学习规则中。如果将这些依赖关系纳入模型,就能发现潜在的目标函数以及生物学习与实验观察比较的细节。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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