Reinforcement Learning: an Overview

By Pierre-Yves Glorennec. 2000

106 citations recorded by the source corpus.

How researchers see this paper

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

1. Yaoyao Liu
2. Bernt Schiele
3. Qianru Sun
4. Mufti Mahmud
5. Lucian Busoniu
6. Robert Babuška
7. Han-Jia Ye
8. M. S. Kaiser

Citations in the corpus

• RMM: Reinforced Memory Management for Class-Incremental Learning (2023, methodology): “Please note that in our case, the ( N + 1 )-phase CIL task is a finite horizon problem [15, 59], so we remove the discounting factor and use R = =0 r t , which is actually the cumulative validation accuracy of all training CIL tasks.”
• Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, background): “Glorennec provides a brief overview of the basis of RL with explicit descriptions of Q- and Fuzzy Q-learning [37].”
• Multi-agent Reinforcement Learning: An Overview (2010, methodology): “Q-learning has been combined with Q-function approximators relying on, e.g., basis-function representations [110,121], neural networks and self-organizing maps [127], and fuzzy rule bases [41,47,56].”