78 citations · 205 across the 14 of their papers we have counts for
7 papers · 1 filter
Strategically Robust Multi-Agent Reinforcement Learning with Linear Function Approximation
Jake Gonzales, Max Horwitz, Eric Mazumdar +1
Provably efficient and robust equilibrium computation in general-sum Markov games remains a core challenge in multi-agent reinforcement learning. Nash equilibrium is computationall…
Who Leads and Who Follows in Strategic Classification?
Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry +1
As predictive models are deployed into the real world, they must increasingly contend with strategic behavior. A growing body of work on strategic classification treats this proble…
Expert Selection in High-Dimensional Markov Decision Processes
Vicenc Rubies-Royo, Eric Mazumdar, Roy Dong +2
In this work we present a multi-armed bandit framework for online expert selection in Markov decision processes and demonstrate its use in high-dimensional settings. Our method tak…
Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games
Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan +1
We show by counterexample that policy-gradient algorithms have no guarantees of even local convergence to Nash equilibria in continuous action and state space multi-agent settings.…
On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games
Eric V. Mazumdar, Michael I. Jordan, S. Shankar Sastry
We propose local symplectic surgery, a two-timescale procedure for finding local Nash equilibria in two-player zero-sum games. We first show that previous gradient-based algorithms…
On Gradient-Based Learning in Continuous Games
Eric Mazumdar, Lillian J. Ratliff, S. Shankar Sastry
We formulate a general framework for competitive gradient-based learning that encompasses a wide breadth of multi-agent learning algorithms, and analyze the limiting behavior of co…
Inverse Risk-Sensitive Reinforcement Learning
Lillian J. Ratliff, Eric Mazumdar
We address the problem of inverse reinforcement learning in Markov decision processes where the agent is risk-sensitive. In particular, we model risk-sensitivity in a reinforcement…