A Bayesian Approach to Generative Adversarial Imitation Learning

By Wonseok Jeon, Seokin Seo, Kee-Eung Kim. 2018

41 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. Yuping Luo
2. Tengyu Ma
3. Huazhe Xu
4. Yuanzhi Li
5. Simon Shaolei Du
6. Zeyuan Allen-Zhu
7. Jason D. Lee
8. Sergey Levine

Citations in the corpus

• Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling (2019, methodology): “The sample efficiency of (Ho & Ermon, 2016) has been improved in various ways, including maximum mean discrepancy minimization (Kim & Park, 2018), a Bayesian formulation of GAIL (Jeon et al., 2018), using an off-policy RL algorithm and solving reward bias problem (Kostrikov et al.”
• A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges (2023, unknown): “[95] Sim State Bayes-GAIL Online No Robotic Locomotion”