306 citations · 2.8k across the 181 of their papers we have counts for
193 papers
Solving Non-Rectangular Reward-Robust MDPs via Frequency Regularization
Uri Gadot, Esther Derman, Navdeep Kumar +3
In robust Markov decision processes (RMDPs), it is assumed that the reward and the transition dynamics lie in a given uncertainty set. By targeting maximal return under the most ad…
Individualized Dosing Dynamics via Neural Eigen Decomposition
Stav Belogolovsky, Ido Greenberg, Danny Eytan +1
Dosing models often use differential equations to model biological dynamics. Neural differential equations in particular can learn to predict the derivative of a process, which per…
Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel
Kaixin Wang, Uri Gadot, Navdeep Kumar +2
Robust Markov Decision Processes (RMDPs) provide a framework for sequential decision-making that is robust to perturbations on the transition kernel. However, current RMDP methods…
CALM: Conditional Adversarial Latent Models for Directable Virtual Characters
Chen Tessler, Yoni Kasten, Yunrong Guo +3
In this work, we present Conditional Adversarial Latent Models (CALM), an approach for generating diverse and directable behaviors for user-controlled interactive virtual character…
Representation-Driven Reinforcement Learning
Ofir Nabati, Guy Tennenholtz, Shie Mannor
We present a representation-driven framework for reinforcement learning. By representing policies as estimates of their expected values, we leverage techniques from contextual band…
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…