scholarly journals Combining Self-organizing Maps with Mixtures of Experts: Application to an Actor-Critic Model of Reinforcement Learning in the Basal Ganglia

2006 ◽  
pp. 394-405 ◽  
Author(s):  
Mehdi Khamassi ◽  
Louis-Emmanuel Martinet ◽  
Agnès Guillot
Keyword(s):  
Reinforcement Learning ◽  
Basal Ganglia ◽  
Self Organizing Maps ◽  
Mixtures Of Experts ◽  
Self Organizing

scholarly journals Self-organizing maps for storage and transfer of knowledge in reinforcement learning

2018 ◽  
Vol 27 (2) ◽  
pp. 111-126 ◽  
Author(s):  
Thommen George Karimpanal ◽  
Roland Bouffanais
Keyword(s):  
Reinforcement Learning ◽  
Self Organizing Map ◽  
Value Functions ◽  
Transfer Of Knowledge ◽  
Network Growth ◽  
Self Organizing Maps ◽  
Task Knowledge ◽  
Novel Approach ◽  
Learning Agent ◽  
Self Organizing

The idea of reusing or transferring information from previously learned tasks (source tasks) for the learning of new tasks (target tasks) has the potential to significantly improve the sample efficiency of a reinforcement learning agent. In this work, we describe a novel approach for reusing previously acquired knowledge by using it to guide the exploration of an agent while it learns new tasks. In order to do so, we employ a variant of the growing self-organizing map algorithm, which is trained using a measure of similarity that is defined directly in the space of the vectorized representations of the value functions. In addition to enabling transfer across tasks, the resulting map is simultaneously used to enable the efficient storage of previously acquired task knowledge in an adaptive and scalable manner. We empirically validate our approach in a simulated navigation environment and also demonstrate its utility through simple experiments using a mobile micro-robotics platform. In addition, we demonstrate the scalability of this approach and analytically examine its relation to the proposed network growth mechanism. Furthermore, we briefly discuss some of the possible improvements and extensions to this approach, as well as its relevance to real-world scenarios in the context of continual learning.

Visualization of Learning Process in “State and Action” Space Using Self-Organizing Maps

2016 ◽  
Vol 20 (6) ◽  
pp. 983-991
Author(s):  
Akira Notsu ◽  
◽  
Yuichi Hattori ◽  
Seiki Ubukata ◽  
Katsuhiro Honda ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Learning Process ◽  
Numerical Experiments ◽  
Action Learning ◽  
Human Learning ◽  
Search Problem ◽  
Self Organizing Maps ◽  
Learning Agents ◽  
Self Organizing

In reinforcement learning, agents can learn appropriate actions for each situation based on the consequences of these actions after interacting with the environment. Reinforcement learning is compatible with self-organizing maps that accomplish unsupervised learning by reacting to impulses and strengthening neurons. Therefore, numerous studies have investigated the topic of reinforcement learning in which agents learn the state space using self-organizing maps. In this study, while we intended to apply these previous studies to transfer the learning and visualization of the human learning process, we introduced self-organizing maps into reinforcement learning and attempted to make their “state and action” learning process visible. We performed numerical experiments with the 2D goal-search problem; our model visualized the learning process of the agent.

A Psychographic Segmentation of Kuwaiti Travelers Using Self-Organizing Maps

2019 ◽  
Vol 24 (1) ◽  
pp. 87-92 ◽  
Author(s):  
Yvette Reisinger ◽  
Mohamed M. Mostafa ◽  
John P. Hayes
Keyword(s):  
Self Organizing Maps ◽  
Self Organizing

Self Organizing Maps, Pattern Recognition and Financial Crises

2011 ◽  
Author(s):  
Sylvain Barthelemy ◽  
Pascal Devaux ◽  
Francois Faure ◽  
Matthieu Pautonnier
Keyword(s):  
Pattern Recognition ◽  
Financial Crises ◽  
Self Organizing Maps ◽  
Self Organizing

Using self organizing maps to analyze larval fish assemblage vertical dynamics through environmental-ontogenetic gradients

2021 ◽  
pp. 107410
Author(s):  
I. Álvarez ◽  
J.S. Font-Muñoz ◽  
I. Hernández-Carrasco ◽  
C. Díaz-Gil ◽  
P.M. Salgado-Hernanz ◽  
...  
Keyword(s):  
Fish Assemblage ◽  
Larval Fish ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 235
Author(s):  
Diego Galvan ◽  
Luciane Effting ◽  
Hágata Cremasco ◽  
Carlos Adam Conte-Junior
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Virus Spread ◽  
Self Organizing Maps ◽  
The North ◽  
Som Clustering ◽  
Unsupervised Neural Networks ◽  
Mining Tools ◽  
Temporal Spread ◽  
Self Organizing

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

scholarly journals Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Adeoluwa Akande ◽  
Ana Cristina Costa ◽  
Jorge Mateu ◽  
Roberto Henriques
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Northern Region ◽  
Extreme Weather Events ◽  
Self Organizing Map ◽  
Spatial And Temporal Patterns ◽  
Self Organizing Maps ◽  
The North ◽  
Precipitation Regimes ◽  
Self Organizing

The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

Sign in / Sign up

Export Citation Format

Share Document