77 citations · 77 across the 2 of their papers we have counts for
6 papers
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…
Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in `Almost All' Zero-Sum Continuous Games
Eric Mazumdar, Lillian Ratliff
We prove that differential Nash equilibria are generic amongst local Nash equilibria in continuous zero-sum games. That is, there exists an open-dense subset of zero-sum games for…
Feedback Linearization for Unknown Systems via Reinforcement Learning
Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar +4
We present a novel approach to control design for nonlinear systems which leverages model-free policy optimization techniques to learn a linearizing controller for a physical plant…
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…