Reinforcement Learning for Learning of Dynamical Systems in Uncertain Environment: a Tutorial
Year: 2019
Type: preprint
Abstract: In this paper, a review of model-free reinforcement learning for learning of dynamical systems in uncertain environments has discussed. For this purpose, the Markov Decision Process (MDP) will be reviewed. Furthermore, some learning algorithms such as Temporal Difference (TD) learning, Q-Learning, and Approximate Q-learning as model-free algorithms which constitute the main part of this article have been investigated, and benefits and drawbacks of each algorithm will be discussed. The discussed concepts in each section are explaining with details and examples.
Source: arXiv (Cornell University)
Authors: Attar, Mehran, Dabirian, Mohammadreza
Institutions: Tarbiat Modares University, Hamedan University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2945036056