Conference Papers Year : 2022

Developmental Modular Reinforcement Learning

Jianyong Xue
Frédéric Alexandre

Abstract

In this article, we propose a modular reinforcement learning (MRL) architecture that coordinates the competition and the cooperation between modules, and inspire, in a developmental approach, the generation of new modules in cases where new goals have been detected. We evaluate the effectiveness of our approach in a multiple-goal torus grid world. Results show that our approach has better performance than previous MRL methods in learning separate strategies for sub-goals, and reusing them for solving task-specific or unseen multi-goal problems, as well as maintaining the independence of the learning in each module.
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Dates and versions

hal-03701184 , version 1 (21-06-2022)

Identifiers

  • HAL Id : hal-03701184 , version 1

Cite

Jianyong Xue, Frédéric Alexandre. Developmental Modular Reinforcement Learning. ESANN2022 - 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Oct 2022, Bruges / Virtual, Belgium. ⟨hal-03701184⟩
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