Regret based Robust Solutions for Uncertain Markov Decision Processes

By Asrar Ahmed, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet. 2013

21 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. Scott Niekum
2. Yinlam Chow
3. Daniel S. Brown
4. Mohammad Ghavamzadeh
5. Marek Petrik
6. Sergey Levine
7. Ofir Nachum
8. Oliver Kroemer

Citations in the corpus

• Safe Policy Improvement by Minimizing Robust Baseline Regret (2016, methodology): “The model-based approach of this paper builds on robust Markov decision processes [Iyengar, 2005; Wiesemann et al., 2013; Ahmed and Varakantham, 2013].”
• Robust Policy Optimization with Baseline Guarantees (2015, background): “What makes our problem different than the standard robust learning and control [1, 6, 8, 9, 11, 13, 19, 22] is the existence of a baseline policy (e.g., current company’s strategy), which often has a reasonable performance.”
• Bayesian Robust Optimization for Imitation Learning (2020, background): “There are similar advantages to optimizing the regret for the optimal policy [2, 3, 44].”