# Reinforcement Learning for Learning of Dynamical Systems in Uncertain Environment: a Tutorial

arXiv: https://arxiv.org/abs/1905.07727 · HTML: https://mlsanity.com/paper/1905.07727/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2019.

## Built on by (most cited first, 2 of 2)

- [CrafText Benchmark: Advancing Instruction Following in Complex Multimodal Open-Ended World](https://mlsanity.com/paper/2505.11962/) (2025)
- [A Dynamical Systems Framework for Reinforcement Learning Safety and Robustness Verification](https://mlsanity.com/paper/2508.15588/) (2025)

## Abstract (arXiv)

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.