Through Cognitive Economy: An Algorithm for Autonomous Representation Learning

Title: Through Cognitive Economy: An Algorithm for Autonomous Representation Learning

Research Question: How can an intelligent agent learn an effective representation for a task by adapting its representation to the requirements of the task as it interacts with the world?

Methodology: The study proposes an algorithm that bases judgments of state compatibility and state-space abstraction on principled criteria derived from the psychological principle of cognitive economy. The algorithm incorporates an active form of Q-learning and partitions continuous state-spaces by merging and splitting Voronoi regions.

Results: The experiments illustrated the algorithm's ability to learn effective representations, superior to those produced by other methods. The results demonstrated the algorithm's success in effectively grouping states and minimizing the prediction error, leading to faster learning and better task performance.

Implications: This research has significant implications for the field of artificial intelligence. It provides a novel approach to autonomous representation learning, which can lead to more efficient and adaptive systems. It also offers new insights into the psychological principle of cognitive economy and its application in reinforcement learning.

Link to Article: https://arxiv.org/abs/0404032v1 Authors: arXiv ID: 0404032v1