Representation Balancing Offline Model-based Reinforcement Learning

By Byung-Jun Lee, Jongmin Lee, Kee-Eung Kim. 2021

52 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. Sergey Levine
2. Tianhe Yu
3. Chelsea Finn
4. Rafael Rafailov
5. Aviral Kumar
6. Aravind Rajeswaran
7. Pieter Abbeel
8. Abhishek Gupta

Citations in the corpus

• COMBO: Conservative Offline Model-Based Policy Optimization (2021, methodology): “Model-based offline RL methods (Finn & Levine, 2017; Ebert et al., 2018; Kahn et al., 2018; Kidambi et al., 2020; Yu et al., 2020c; Matsushima et al., 2020; Argenson & DulacArnold, 2020; Swazinna et al., 2020; Rafailov et al., 2020; Lee et al., 2021) provide an alternative approach to policy learnin”