78 citations · 205 across the 14 of their papers we have counts for
4 papers · 1 filter
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.…
Convergence Analysis of Gradient-Based Learning with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings
Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar +1
Considering a class of gradient-based multi-agent learning algorithms in non-cooperative settings, we provide local convergence guarantees to a neighborhood of a stable local Nash…
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…