Using Data Augmentation for Vision-Based Deep Reinforcement Learning
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 100
Special Issue Editors
Interests: machine learning; artificial intelligence; big data; data analysis
Special Issue Information
Dear Colleagues,
In recent years, vision-based deep reinforcement learning (RL) has emerged as a powerful approach for solving complex decision-making tasks by leveraging visual data. However, the performance and generalization of deep RL models can be significantly hampered by the limited availability of diverse and representative training data. This Special Issue focuses on the innovative application of data augmentation techniques to enhance vision-based deep RL. Data augmentation, which encompasses techniques such as image transformations, synthetic data generation, and domain randomisation, can enhance the robustness and versatility of models by artificially increasing the variability of training datasets. This Special Issue explores a wide range of augmentation strategies and their impact on the learning efficiency, stability, and generalisation capabilities of RL agents. Contributions include theoretical insights, novel algorithms, and practical applications across various domains. The aim of this Special Issue is to facilitate the advancement of research in the field of vision-based deep RL by addressing critical challenges and opening new avenues for investigation.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Techniques and methodologies for data augmentation in vision-based systems.
- Applications of RL in environments with visual input.
- Performance comparison of RL models with and without data augmentation.
- The impact of synthetic and real-world data on the learning efficiency and accuracy of RL systems.
- Case studies detailing the implementation of vision-based RL systems in various domains.
- Theoretical insights or reviews on the convergence properties of augmented RL algorithms.
- Innovations in hardware and software that enhance the training of RL systems using augmented data.
We look forward to receiving your contributions.
Dr. Kai Vahldiek
Prof. Dr. Dirk Joachim Lehmann
Guest Editors
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Keywords
- data augmentation
- vision-based deep reinforcement learning
- image transformations
- synthetic data generation
- domain randomization
- learning efficiency
- generalization in reinforcement learning
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