A reinforcement learning based neural network architecture for obstacle avoidance in multi-fingered grasp synthesis

2009 ◽  
Vol 72 (4-6) ◽  
pp. 1229-1241 ◽  
Author(s):  
Nasser Rezzoug ◽  
Philippe Gorce
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Obstacle Avoidance ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Grasp Synthesis

scholarly journals A Parametric Study of a Deep Reinforcement Learning Control System Applied to the Swing-Up Problem of the Cart-Pole

2020 ◽  
Vol 10 (24) ◽  
pp. 9013
Author(s):  
Camilo Andrés Manrique Escobar ◽  
Carmine Maria Pappalardo ◽  
Domenico Guida
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Dry Friction ◽  
Network Architecture ◽  
Control Policy ◽  
Neural Network Architecture ◽  
Learning Capability ◽  
Friction Forces ◽  
The Neural Network ◽  
Pole System

In this investigation, the nonlinear swing-up problem associated with the cart-pole system modeled as a multibody dynamical system is solved by developing a deep Reinforcement Learning (RL) controller. Furthermore, the sensitivity analysis of the deep RL controller applied to the cart-pole swing-up problem is carried out. To this end, the influence of modifying the physical properties of the system and the presence of dry friction forces are analyzed employing the cumulative reward during the task. Extreme limits for the modifications of the parameters are determined to prove that the neural network architecture employed in this work features enough learning capability to handle the task under modifications as high as 90% on the pendulum mass, as well as a 100% increment on the cart mass. As expected, the presence of dry friction greatly affects the performance of the controller. However, a post-training of the agent in the modified environment takes only thirty-nine episodes to find the optimal control policy, resulting in a promising path for further developments of robust controllers.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

scholarly journals Learning Subject-Generalized Topographical EEG Embeddings Using Deep Variational Autoencoders and Domain-Adversarial Regularization

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1792
Author(s):  
Juan Hagad ◽  
Tsukasa Kimura ◽  
Ken-ichi Fukui ◽  
Masayuki Numao
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Emotion Classification ◽  
Limited Data ◽  
Neural Network Architecture ◽  
Building Models ◽  
The Subject ◽  
Data Constraints ◽  
Input Level ◽  
Normally Distributed

Two of the biggest challenges in building models for detecting emotions from electroencephalography (EEG) devices are the relatively small amount of labeled samples and the strong variability of signal feature distributions between different subjects. In this study, we propose a context-generalized model that tackles the data constraints and subject variability simultaneously using a deep neural network architecture optimized for normally distributed subject-independent feature embeddings. Variational autoencoders (VAEs) at the input level allow the lower feature layers of the model to be trained on both labeled and unlabeled samples, maximizing the use of the limited data resources. Meanwhile, variational regularization encourages the model to learn Gaussian-distributed feature embeddings, resulting in robustness to small dataset imbalances. Subject-adversarial regularization applied to the bi-lateral features further enforces subject-independence on the final feature embedding used for emotion classification. The results from subject-independent performance experiments on the SEED and DEAP EEG-emotion datasets show that our model generalizes better across subjects than other state-of-the-art feature embeddings when paired with deep learning classifiers. Furthermore, qualitative analysis of the embedding space reveals that our proposed subject-invariant bi-lateral variational domain adversarial neural network (BiVDANN) architecture may improve the subject-independent performance by discovering normally distributed features.

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