11 citations · 12 across the 21 of their papers we have counts for
22 papers
Provably Safe Sim-to-Real Transfer
Tingting Ni, Maryam Kamgarpour
To mitigate the sample complexity of real-world reinforcement learning (RL), a common practice is to first train a policy in a simulator, where samples are cheap, and then deploy t…
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics
Philip Jordan, Maryam Kamgarpour
We study Nash equilibrium learning in partially observable Markov games (POMGs), a multi-agent reinforcement learning framework in which agents cannot fully observe the underlying…
Fast Rates for Inverse Reinforcement Learning
Andreas Schlaginhaufen, Maryam Kamgarpour
We establish novel structural and statistical results for entropy-regularized min-max inverse reinforcement learning (Min-Max-IRL) in finite-horizon MDPs with Borel state and actio…
Model-Based Learning of Near-Optimal Finite-Window Policies in POMDPs
Philip Jordan, Maryam Kamgarpour
We study model-based learning of finite-window policies in tabular partially observable Markov decision processes (POMDPs). A common approach to learning under partial observabilit…
Constrained Meta Reinforcement Learning with Provable Test-Time Safety
Tingting Ni, Maryam Kamgarpour
Meta reinforcement learning (RL) allows agents to leverage experience across a distribution of tasks on which the agent can train at will, enabling faster learning of optimal polic…
Eliciting Truthful Feedback for Preference-Based Learning via the VCG Mechanism
Leo Landolt, Anna Maddux, Andreas Schlaginhaufen +2
We study resource allocation problems in which a central planner allocates resources among strategic agents with private cost functions in order to minimize a social cost, defined…