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20162025
most citedChance-Constrained Trajectory Planning with Multimodal Environmental Uncertainty

29 citations · 98 across the 27 of their papers we have counts for

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Showing cs.LGShow all

5 papers · 1 filter

cs.LG2023★ 2 cited

Identifiability and Generalizability in Constrained Inverse Reinforcement Learning

Andreas Schlaginhaufen, Maryam Kamgarpour

Two main challenges in Reinforcement Learning (RL) are designing appropriate reward functions and ensuring the safety of the learned policy. To address these challenges, we present…

cs.LG2022

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Pier Giuseppe Sessa, Maryam Kamgarpour, Andreas Krause

We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environmen…

cs.LG2020★ 1 cited

Learning to Play Sequential Games versus Unknown Opponents

Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour +1

We consider a repeated sequential game between a learner, who plays first, and an opponent who responds to the chosen action. We seek to design strategies for the learner to succes…

cs.LG2020★ 1 cited

Mixed Strategies for Robust Optimization of Unknown Objectives

Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour +1

We consider robust optimization problems, where the goal is to optimize an unknown objective function against the worst-case realization of an uncertain parameter. For this setting…

cs.LG2019

No-Regret Learning in Unknown Games with Correlated Payoffs

Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour +1

We consider the problem of learning to play a repeated multi-agent game with an unknown reward function. Single player online learning algorithms attain strong regret bounds when p…