A Modular Reinforcement Learning Architecture for Mobile Robot Control
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.
KeywordsMobile Robot Autonomous Agent Recurrent Connection Modular Neural Network Credit Assignment
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