BSc. Thesis, Weiyi Lin – Image augmentation and its impacts on reinforcement learning models
Supervisor: Vedant Dave, M.Sc.
Univ.-Prof. Dr Elmar Rückert
Start date: 3rd April 2025
Theoretical difficulty: mid
Practical difficulty: low
Abstract
Due to the tendency of reinforcement learning models to overfit to training data, data augmentation has become a widely adopted technique for visual reinforcement learning tasks for its capability of enhancing the performance and generalization of agents by increasing the diversity of training data. Often, different tasks benefit from different types of augmentations, and selecting them requires prior knowledge of the environment. This thesis aims to explore how various augmentation strategies can impact the performance and generalization of agents in visual environments, including visual augmentations and context-aware augmentations.
Tentative Work Plan
- Literature research.
- Understanding of concepts of visual RL models (SVEA).
- Implementing and testing different augmentations.
- Observation and documentation of results.
- Thesis writing.
Related Work
[1] N. Hansen and X. Wang, “Generalization in Reinforcement Learning by Soft Data Augmentation,” 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, 2021, pp. 13611-13617, doi: 10.1109/ICRA48506.2021.9561103
[2] Hansen, Nicklas, Hao Su, and Xiaolong Wang. “Stabilizing deep q-learning with convnets and vision transformers under data augmentation.” Advances in neural information processing systems 34 (2021): 3680-3693.
[3] Almuzairee, Abdulaziz, Nicklas Hansen, and Henrik I. Christensen. “A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning.” Reinforcement Learning Conference.
