Advanced topics in deep reinforcement learning and its applications - Chen, Jianda (Nanyang Technological University, 2023)
The development of reinforcement learning attracts more and more attention among researchers. Leveraging deep learning, i.e. embedding neural network function approximators, reinforcement learning is empowered to achieve great success on a broad range of tasks including video games, control, Natural Language Processing (NLP), and data center cooling. However, applications of deep reinforcement learning under a real-world setting usually suffer from an overfitting issue, in which performance would likely decrease if the environment is slightly different from the training environment. It turns out that it is crucial to improve deep reinforcement learning’s generalization ability such that its techniques can be applied to real-world scenarios more effectively.