Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning

By Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor. 2018

27 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. Matteo Hessel
2. John Aslanides
3. Hado van Hasselt
4. Pieter Abbeel
5. Sergey Levine
6. David Silver
7. Marc G. Bellemare
8. R´emi Munos

Citations in the corpus

• When to use parametric models in reinforcement learning? (2019, methodology): “This process can be interpreted as using a multi-step greedy policy [Efroni et al., 2018] to determine behaviour, instead of the more standard one-step greedy policy.”