Reinforcement Learning and Biologically Inspired Artificial Neural Networks
Communications in Computer and Information Science, ISSN: 1865-0937, Vol: 2142 CCIS, Page: 62-79
2024
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Over the last few years, machine learning methods have used Deep Neural Network architectures to tackle complex problems. In this paper, we applied biologically inspired neural machine learning to solve two classical and well-known challenging problems, the Mountain Car Continuous and the Cart Pole. We use a neural network extracted from the connectome of C-Elegans to learn a policy able to yield a good solution. We used Reinforcement Learning (RL) and optimization techniques to train the models, in addition to proposing a novel neural dynamics model. We use different metrics to make a detailed comparison of the results obtained, combining different neuronal dynamics and optimization methods. We obtained very competitive results compared with the solution provided in the literature, particularly with the novel dynamic neuronal model.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85199649881&origin=inward; http://dx.doi.org/10.1007/978-3-031-63616-5_5; https://link.springer.com/10.1007/978-3-031-63616-5_5; https://dx.doi.org/10.1007/978-3-031-63616-5_5; https://link.springer.com/chapter/10.1007/978-3-031-63616-5_5
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know