Structure in Deep Reinforcement Learning: A Survey and Open Problems

arXiv:2306.16021v3 Announce Type: replace
Abstract: Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural Networks (DNNs) for function approximation, has demonstrated considerable success in numerous applications. However, its practicality in addressing various real-world scenarios, characterized by diverse and unpredictable dynamics, noisy signals, and large state and action spaces, remains limited. This limitation stems from poor data efficiency, limited generalization capabilities, a lack of safety guarantees, and the absence of interpretability, among other factors. To overcome these challenges and …

abstract applications approximation arxiv capabilities cs.ai cs.lg diverse dynamics function however networks neural networks reinforcement reinforcement learning spaces state success survey type world