On-chip trainable hardware-based deep Q-networks approximating a backpropagation algorithm
On-chip trainable hardware-based deep Q-networks approximating a backpropagation algorithm
About this item
Full title
Author / Creator
Publisher
London: Springer London
Journal title
Language
English
Formats
Publication information
Publisher
London: Springer London
Subjects
More information
Scope and Contents
Contents
Reinforcement learning (RL) using deep Q-networks (DQNs) has shown performance beyond the human level in a number of complex problems. In addition, many studies have focused on bio-inspired hardware-based spiking neural networks (SNNs) given the capabilities of these technologies to realize both parallel operation and low power consumption. Here, w...
Alternative Titles
Full title
On-chip trainable hardware-based deep Q-networks approximating a backpropagation algorithm
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2549110075
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2549110075
Other Identifiers
ISSN
0941-0643
E-ISSN
1433-3058
DOI
10.1007/s00521-021-05699-z
