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A Modular Reinforcement Learning Architecture for Mobile Robot Control

  • R. M. Rylatt
  • C. A. Czarnecki
  • T. W. Routen
Conference paper

Abstract

The paper presents a way of extending complementary reinforcement backpropagation learning (CRBP) to modular architectures using a new version of the gating network approach in the context of reactive navigation tasks for a simulated mobile robot. The gating network has partially recurrent connections to enable the co-ordination of reinforcement learning across both modules successive time steps. The experiments reported explore the possibility that architectures based on this approach can support concurrent acquisition of different reactive navigation related competences while the robot pursues light-seeking goals.

Keywords

Mobile Robot Autonomous Agent Recurrent Connection Modular Neural Network Credit Assignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • R. M. Rylatt
    • 1
  • C. A. Czarnecki
    • 1
  • T. W. Routen
    • 1
  1. 1.Department of Computer ScienceDe Montfort UniversityLeicesterUK

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