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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 1: AAAI-22 Technical Tracks 1

Multi-Sacle Dynamic Coding Improved Spiking Actor Network for Reinforcement Learning

February 1, 2023

Authors

Duzhen Zhang

Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences


Tielin Zhang

Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences


Shuncheng Jia

Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences


Bo Xu

Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences


Proceedings:

No. 1: AAAI-22 Technical Tracks 1

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Cognitive Modeling & Cognitive Systems

Downloads:

Download PDF

Abstract:

With the help of deep neural networks (DNNs), deep reinforcement learning (DRL) has achieved great success on many complex tasks, from games to robotic control. Compared to DNNs with partial brain-inspired structures and functions, spiking neural networks (SNNs) consider more biological features, including spiking neurons with complex dynamics and learning paradigms with biologically plausible plasticity principles. Inspired by the efficient computation of cell assembly in the biological brain, whereby memory-based coding is much more complex than readout, we propose a multiscale dynamic coding improved spiking actor network (MDC-SAN) for reinforcement learning to achieve effective decision-making. The population coding at the network scale is integrated with the dynamic neurons coding (containing 2nd-order neuronal dynamics) at the neuron scale towards a powerful spatial-temporal state representation. Extensive experimental results show that our MDC-SAN performs better than its counterpart deep actor network (based on DNNs) on four continuous control tasks from OpenAI gym. We think this is a significant attempt to improve SNNs from the perspective of efficient coding towards effective decision-making, just like that in biological networks.

DOI:

10.1609/aaai.v36i1.19879


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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