Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics
Abstract
We introduce a dynamic neural algorithm called Dynamic Neural (DN) SARSA(\lambda) for learning a behavioral sequence from delayed reward. DN-SARSA(\lambda) 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(\lambda) 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(\lambda) performs on the level of the discrete SARSA(\lambda), validating the feasibility of general reinforcement learning without compromising neural dynamics.
Keywords
Cite
@article{arxiv.1210.3569,
title = {Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics},
author = {Sohrob Kazerounian and Matthew Luciw and Mathis Richter and Yulia Sandamirskaya},
journal= {arXiv preprint arXiv:1210.3569},
year = {2013}
}
Comments
Sohrob Kazerounian, Matthew Luciw are Joint first authors