Noisy reinforcements in reinforcement learning: some case studies based on gridworlds

By A. Moreno, José D. Martín, E. Soria, R. Magdalena, Marcelino Martínez. 2006

14 citations recorded by the source corpus.

Subfields: Reinforcement Learning

How researchers see this paper

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

In Reinforcement Learning

1. Roy Fox
2. Ari Pakman
3. Naftali Tishby
4. Sergey Levine
5. Pieter Abbeel
6. Lihong Li
7. Dale Schuurmans
8. Ofir Nachum

Citations in the corpus

• G-Learning: Taming the Noise in Reinforcement Learning via Soft Updates (2015, background): “For other works that study the effect of noise in Q-learning, although without identifying the bias (7), see [27, 28, 29].”