Strengths, Weaknesses, and Combinations of Model-based and Model-free Reinforcement Learning
By Kavosh Asadi. 2015
8 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.
- Sergey Levine
- Anusha Nagabandi
- Pieter Abbeel
- Gregory Kahn
- Michael Janner
- Chelsea Finn
- Abhishek Gupta
- Ronald S. Fearing
Citations in the corpus
- Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning (2017, methodology): “This method was extended [33], [34] to work with deep neural network policies, but performed best with models that were not neural networks [9].”