Motivated learning as an extension of reinforcement learning

Motivated learning as an extension of reinforcement learning

We have developed a unified framework to conduct computational experiments with both learning systems: Motivated learning based on Goal Creation System, and reinforcedment learning using RL Q-Learning Algorithm. Future work includes combining motivated learning to set abstract motivations and manage...

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Bibliographic Details
Main Authors: STARZYK, Janusz, RAIF, Pawel, TAN, Ah-hwee
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2010
Subjects:
Learning systems
motivated learning
reinforcement learning
Artificial Intelligence and Robotics
Theory and Algorithms
Online Access:https://ink.library.smu.edu.sg/sis_research/6573
https://ink.library.smu.edu.sg/context/sis_research/article/7576/viewcontent/10.1.1.418.3487.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:We have developed a unified framework to conduct computational experiments with both learning systems: Motivated learning based on Goal Creation System, and reinforcedment learning using RL Q-Learning Algorithm. Future work includes combining motivated learning to set abstract motivations and manage goals with reinforcement learning to learn proper actions. This will allow testing of motivated learning on typical reinforcement learning benchmarks with large dimensionality of the state/action spaces.

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