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arXiv · 1210.3569

Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics

Sohrob Kazerounian·Matthew Luciw·Mathis Richter·Yulia Sandamirskaya
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Abstract

We introduce a dynamic neural algorithm called Dynamic Neural (DN) SARSA(λ) for learning a behavioral sequence from delayed reward. DN-SARSA(λ) combines Dynamic Field Theory models of behavioral sequence representation, classical reinforcement learning, and a computational neuroscience model of working memory, called Item and Order working memory, which serves as an eligibility trace. DN-SARSA(λ) is implemented on both a simulated and real robot that must learn a specific rewarding sequence of elementary behaviors from exploration. Results show DN-SARSA(λ) performs on the level of the discrete SARSA(λ), validating the feasibility of general reinforcement learning without compromising neural dynamics.

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Sohrob Kazerounian, Matthew Luciw, Mathis Richter, Yulia Sandamirskaya. 2013-05-14. Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics. https://arxiv.org/abs/1210.3569

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