1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence Analysis
Jia Lin Hau, Erick Delage, Esther Derman +2
In Markov decision processes (MDPs), quantile risk measures such as Value-at-Risk are a standard metric for modeling RL agents' preferences for certain outcomes. This paper propose…
cs.AI2024★ 1 cited
Tree Search-Based Policy Optimization under Stochastic Execution Delay
David Valensi, Esther Derman, Shie Mannor +1
The standard formulation of Markov decision processes (MDPs) assumes that the agent's decisions are executed immediately. However, in numerous realistic applications such as roboti…
cs.LG2023
Twice Regularized Markov Decision Processes: The Equivalence between Robustness and Regularization
Esther Derman, Yevgeniy Men, Matthieu Geist +1
Robust Markov decision processes (MDPs) aim to handle changing or partially known system dynamics. To solve them, one typically resorts to robust optimization methods. However, thi…