11 citations · 12 across the 21 of their papers we have counts for
25 papers
Cooperative Multi-Agent Reinforcement Learning with Submodular Trajectory Rewards
Yasaman Zolfimoselo, Nikolas Geroliminis, Maryam Kamgarpour
We study cooperative multi-agent reinforcement learning with decoupled dynamics and a monotone submodular reward function over joint trajectories. Due to the non-additive reward, a…
Inferring Transferable Rewards via Active Inverse Reinforcement Learning
Victor Villin, Till Freihaut, Andreas Schlaginhaufen +3
Inverse Reinforcement Learning (IRL) aims to recover expert rewards that transfer across environments with differing dynamics. Prior work has shown that such transfer is possible w…
Finite-Time Guarantees for Constrained Reinforcement Learning under Partial Observability
Valentina Zaccaria, Philip Jordan, Tingting Ni +2
We study reinforcement learning in constrained partially observable Markov decision processes (CPOMDPs), where an agent maximizes reward subject to a cost constraint using only inc…
Provably Safe Sim-to-Real Transfer
Tingting Ni, Maryam Kamgarpour
We address safe sim-to-real transfer, in which an agent leverages an imperfect simulator and limited real-world interaction while ensuring safety throughout data collection in the…
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…
Nash Equilibria in Games with Playerwise Concave Coupling Constraints: Existence and Computation
Philip Jordan, Maryam Kamgarpour
We study the existence and computation of Nash equilibria in concave games where the players' admissible strategies are subject to shared coupling constraints. Under playerwise con…
Convergence Rate of Generalized Nash Equilibrium Learning in Strongly Monotone Games with Linear Constraints
Tatiana Tatarenko, Maryam Kamgarpour
We consider payoff-based learning of a generalized Nash equilibrium (GNE) in multi-agent systems. Our focus is on games with jointly convex constraints of a linear structure and st…
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…
Efficient Preference-Based Reinforcement Learning: Randomized Exploration Meets Experimental Design
Andreas Schlaginhaufen, Reda Ouhamma, Maryam Kamgarpour
We study reinforcement learning from human feedback in general Markov decision processes, where agents learn from trajectory-level preference comparisons. A central challenge in th…
Selling Information to a Competitor
Thomas Falconer, Anubhav Ratha, Jalal Kazempour +2
Selling information to a competitor can generate revenue but may also help the rival choose better actions in the market they compete in. We study this trade-off when the buyer's d…
No-Regret Learning in Stackelberg Games with an Application to Electric Ride-Hailing
Anna Maddux, Marko Maljkovic, Nikolas Geroliminis +1
We consider the problem of efficiently learning to play single-leader multi-follower Stackelberg games when the leader lacks knowledge of the lower-level game. Such games arise in…
A learning-based approach to stochastic optimal control under reach-avoid constraint
Tingting Ni, Maryam Kamgarpour
We develop a model-free approach to optimally control stochastic, Markovian systems subject to a reach-avoid constraint. Specifically, the state trajectory must remain within a saf…
Convergence Rate of Payoff-based Generalized Nash Equilibrium Learning
Tatiana Tatarenko, Maryam Kamgarpour
We consider generalized Nash equilibrium (GNE) problems in games with strongly monotone pseudo-gradients and jointly linear coupling constraints. We establish the convergence rate…
Nash equilibria in scalar discrete-time linear quadratic games
Giulio Salizzoni, Reda Ouhamma, Maryam Kamgarpour
An open problem in linear quadratic (LQ) games has been characterizing the Nash equilibria. This problem has renewed relevance given the surge of work on understanding the converge…
Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning
Andreas Schlaginhaufen, Maryam Kamgarpour
Inverse reinforcement learning (IRL) aims to infer a reward from expert demonstrations, motivated by the idea that the reward, rather than the policy, is the most succinct and tran…
Finite-time convergence to an -efficient Nash equilibrium in potential games
Anna Maddux, Reda Ouhamma, Maryam Kamgarpour
This paper investigates the convergence time of log-linear learning to an -efficient Nash equilibrium in potential games, where an efficient Nash equilibrium is defined as the m…
Learning to Bid in Forward Electricity Markets Using a No-Regret Algorithm
Arega Getaneh Abate, Dorsa Majdi, Jalal Kazempour +1
It is a common practice in the current literature of electricity markets to use game-theoretic approaches for strategic price bidding. However, they generally rely on the assumptio…
Convergence of a model-free entropy-regularized inverse reinforcement learning algorithm
Titouan Renard, Andreas Schlaginhaufen, Tingting Ni +1
Given a dataset of expert demonstrations, inverse reinforcement learning (IRL) aims to recover a reward for which the expert is optimal. This work proposes a model-free algorithm t…
Recursively Feasible Chance-constrained Model Predictive Control under Gaussian Mixture Model Uncertainty
Kai Ren, Colin Chen, Hyeontae Sung +3
We present a chance-constrained model predictive control (MPC) framework under Gaussian mixture model (GMM) uncertainty. Specifically, we consider the uncertainty that arises from…
Learning in Zero-Sum Markov Games: Relaxing Strong Reachability and Mixing Time Assumptions
Reda Ouhamma, Maryam Kamgarpour
We address payoff-based decentralized learning in infinite-horizon zero-sum Markov games. In this setting, each player makes decisions based solely on received rewards, without obs…