Paper - A Procedural Constructive Learning Mechanism with Deep Reinforcement Learning for Cognitive Agents
Work published in 2024 at JINT Journal of Intelligent & Robotic Systems. presents a learning strategy that amalgamates deep reinforcement learning with procedural learning, mirroring the incremental learning process observed in human sensorimotor development. This approach is embedded within the CONAIM cognitive-attentional architecture, leveraging the cognitive tools of CST.
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