Multi-Scale Dynamic Coding Improved Spiking Actor Network for Reinforcement Learning | |
Zhang, Duzhen2,3; Zhang, Tielin2,3; Jia, Shuncheng2,3; Xu, Bo1,2,3 | |
2022-06-28 | |
会议日期 | February 22–March 1, 2022 |
会议地点 | Online |
英文摘要 | 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. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/57352] ![]() |
专题 | 数字内容技术与服务研究中心_听觉模型与认知计算 |
通讯作者 | Zhang, Tielin; Xu, Bo |
作者单位 | 1.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China 2.Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | Zhang, Duzhen,Zhang, Tielin,Jia, Shuncheng,et al. Multi-Scale Dynamic Coding Improved Spiking Actor Network for Reinforcement Learning[C]. 见:. Online. February 22–March 1, 2022. |
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