Ben Chasnov

PhD student at the University of Washington

Broadly speaking, my research interest is how optimizers interact with each other and with humans. More specifically, we study agents that use gradients to solve for the optimal actions given the (modeled or observed) actions of others. These settings have wide ranging applications in robust control and robust machine learning, human/machine interaction and computational neuroscience. My advisers are Sam Burden and Lillian Ratliff. I collaborate with the Autonomous Control Laboratory and the Computational Neuroscience Center community at UW.

My prior research at the Lab for Autonomous and Intelligent Robotics (LAIR) at Harvey Mudd College was on cooperative multi-agent underwater robotics and aerial vehicles, with an emphasis of deploying robot systems in harsh environments such as confined caves and open waters.

Working Papers

Learning in Human/Machine Systems

with Lillian J. Ratliff and Samuel A. Burden


Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms

with Liyuan Zheng, Tanner Fiez, Zane Alumbaugh, Benjamin Chasnov and Lillian Ratliff

In: Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), Feb. 2022.

Visual Modeling System for Real-Time Optimal Trajectory Planning for Autonomous Aerial Drones

with Skye Mceowen, Daniel Sullivan, Benjamin Chasnov, Dan Calderone, Oliver Sheridan and Behcet Acikmese

In: IEEE Aerospace Conference (AeroConf), Mar 2022.

Stability of Gradient Learning Dynamics in Continuous Games:

Scalar Action Spaces

with Dan Calderone, Behcet Acikmese, Samuel A. Burden, Lillian J. Ratliff

In IEEE Conference on Decision and Control (CDC), Dec 2020.


Implicit Learning Dynamics in Stackelberg Games:

Equilibria Characterization, Convergence Analysis, and Empirical Study

with Tanner Fiez and Lillian J. Ratliff

In Thirty-seventh International Conference on Machine Learning (ICML), July 2020.


Convergence Analysis of Gradient-Based Learning in Continuous Games

with Lillian J. Ratliff, Eric Mazumdar, Samuel A. Burden.

In Uncertainty in Artificial Intelligence (UAI), pp. 935-944. Proceedings of Machine Learning Research, 2019.


Experiments with Sensorimotor Games in Dynamic Human/Machine Interaction.

with Momona Yamagami, Behnoosh Parsa, Lillian J. Ratliff, Samuel A. Burden.

In Proceedings of SPIE Micro- and Nanotechnology Sensors, Systems, and Applications XI, May 2019.


Towards Three-Dimensional Underwater Mapping Without Odometry.

with Alistair Dobke, Joshua Vasquez, Lauren Lieu, Christopher Clark, Ian Dunn, Zoe J. Wood, and Timothy Gambin.

In Unmanned Untethered Submersible Technology Conference 2013 Proceedings: Portsmouth, NH. 2013.



Stability of Gradient Learning Dynamics in Continuous Games:

Vector Action Spaces

with Dan Calderone, Behcet Acikmese, Samuel A. Burden, Lillian J. Ratliff


Workshop Papers

Opponent Anticipation via Conjectural Variations

with Tanner Fiez and Lillian J. Ratliff.

In NeurIPS Smooth Games Optimization and Machine Learning Workshop: Bridging Game Theory and Deep Learning, Dec 2019.


Finite-Time Convergence of Gradient-Based Learning in Continuous Games.

with Lillian J. Ratliff, Daniel Calderone, Eric Mazumdar, and Samuel A. Burden.

In AAAI Workshop on Reinforcement Learning in Games, Jan 2019.



I’ve given oral presentations at