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Metrics for assessing generalization of deep reinforcement learning in parameterized environments

Aleksandrowicz Maciej, Jaworek-Korjakowska Joanna

Journal of Artificial Intelligence and Soft Computing Research

2024

Vol. 14, No. 1

45--61

In this work, a study focusing on proposing generalization metrics for Deep Reinforcement Learning (DRL) algorithms was performed. The experiments were conducted in DeepMind Control (DMC) benchmark suite with parameterized environments. The performance of three DRL algorithms in selected ten tasks from the DMC suite has been analysed with existing generalization gap formalism and the proposed ratio and decibel metrics. The results were presented with the proposed methods: average transfer metric and plot for environment normal distribution. These efforts allowed to highlight major changes in the model’s performance and add more insights about making decisions regarding models’ requirements.

An intelligent approach to short-term wind power prediction using deep neural networks

Niksa-Rynkiewicz Tacjana, Stomma Piotr, Witkowska Anna, Rutkowska Danuta, Słowik Adam, Cpałka Krzysztof, Jaworek-Korjakowska Joanna, Kolendo Piotr

Journal of Artificial Intelligence and Soft Computing Research

2023

Vol. 13. No. 3

197--210

In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent Unit), and H-MLP (Hierarchical Multilayer Perceptron). The DNN architectures are part of the Deep Learning Prediction (DLP) framework that is applied in the Deep Learning Power Prediction System (DLPPS). The system is trained based on data that comes from a real wind farm. This is significant because the prediction results strongly depend on weather conditions in specific locations. The results obtained from the proposed system, for the real data, are presented and compared. The best result has been achieved for the GRU network. The key advantage of the system is a high effectiveness prediction using a minimal subset of parameters. The prediction of wind power in wind farms is very important as wind power capacity has shown a rapid increase, and has become a promising source of renewable energies.

Transfer Learning Methods as a New Approach in Computer Vision Tasks with Small Datasets

Brodzicki Andrzej, Piekarski Michał, Kucharski Dariusz, Jaworek-Korjakowska Joanna, Gorgon Marek

Foundations of Computing and Decision Sciences

2020

Vol. 45, No. 3

179--193

Deep learning methods, used in machine vision challenges, often face the problem of the amount and quality of data. To address this issue, we investigate the transfer learning method. In this study, we briefly describe the idea and introduce two main strategies of transfer learning. We also present the widely-used neural network models, that in recent years performed best in ImageNet classification challenges. Furthermore, we shortly describe three different experiments from computer vision field, that confirm the developed algorithms ability to classify images with overall accuracy 87.2-95%. Achieved numbers are state-of-the-art results in melanoma thick- ness prediction, anomaly detection and Clostridium difficile cytotoxicity classification problems