19 papers
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…
On the characterization of constrained correlated equilibria in Markov games
Tingting Ni, Anna Maddux, Maryam Kamgarpour
Markov games with coupling constraints model constrained dynamical decision-making involving self-interested agents, where the feasibility of an individual agent's strategy depends…
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…
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…
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…
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…