Reinforcement Learning: A Tutorial.

By Mance E. Harmon, Stephanie Harmon. 1997

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Anthony J. Jakeman
2. Serena H. Chen
3. John P. Norton
4. Florentin Wörgötter
5. Bernd Porr
6. Huizhen Yu
7. J. Urgen Schmidhuber
8. Aviv Tamar

Citations in the corpus

• Reinforcement learning (2019, positive): “Modern RL sits in the intersection of many diverse disciplines and we take an approach based on machine learning (Sutton and Barto, 2018; Szepesv´ari, 2010) to provide this introduction.”
• Learning classifier systems: a complete introduction, review, and roadmap (2009, unknown): “For a detailed introduction to RL we refer readers to Sutton and Barto (1998) [15], Harmon (1996) [16], and Wyatt (2005) [17].”
• Artificial Intelligence techniques: An introduction to their use for modelling environmental systems (2008, background): “An RL problem has three parts: environment, reinforcement function and value function [73].”