23 citations · 78 across the 10 of their papers we have counts for
11 papers
State Entropy Regularization for Robust Reinforcement Learning
Yonatan Ashlag, Uri Koren, Mirco Mutti +3
State entropy regularization has empirically shown better exploration and sample complexity in reinforcement learning (RL). However, its theoretical guarantees have not been studie…
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…
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…
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…
Policy Gradient for Rectangular Robust Markov Decision Processes
Navdeep Kumar, Esther Derman, Matthieu Geist +2
Policy gradient methods have become a standard for training reinforcement learning agents in a scalable and efficient manner. However, they do not account for transition uncertaint…
Twice regularized MDPs and the equivalence between robustness and regularization
Esther Derman, Matthieu Geist, Shie Mannor
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…