Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision Problems. - Ekaterina I. Tolstaya; Alec Koppel; Ethan Stump; Alejandro Ribeiro (ACC, 2018)

Composable Learning with Sparse Kernel Representations. - Ekaterina I. Tolstaya; Ethan Stump; Alec Koppel; Alejandro Ribeiro (IROS, 2018)

Graph Policy Gradients for Large Scale Robot Control. - Arbaaz Khan; Ekaterina I. Tolstaya; Alejandro Ribeiro; Vijay Kumar 001 (CoRL, 2019)

Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks. - Ekaterina I. Tolstaya; Fernando Gama; James Paulos; George J. Pappas; Vijay Kumar 001; Alejandro Ribeiro (CoRL, 2019)

Inverse Optimal Planning for Air Traffic Control. - Ekaterina I. Tolstaya; Alejandro Ribeiro; Vijay Kumar 001; Ashish Kapoor (IROS, 2019)

Decentralized Control with Graph Neural Networks. - Fernando Gama; Qingbiao Li; Ekaterina I. Tolstaya; Amanda Prorok; Alejandro Ribeiro (CoRR, 2020)

Graph Neural Networks for Decentralized Controllers. - Fernando Gama; Ekaterina I. Tolstaya; Alejandro Ribeiro (ICASSP, 2021)

Identifying Driver Interactions via Conditional Behavior Prediction. - Ekaterina I. Tolstaya; Reza Mahjourian; Carlton Downey; Balakrishnan Varadarajan; Benjamin Sapp; Dragomir Anguelov (ICRA, 2021)

Learning Connectivity for Data Distribution in Robot Teams. - Ekaterina I. Tolstaya; Landon Butler; Daniel Mox; James Paulos; Vijay Kumar 001; Alejandro Ribeiro (IROS, 2021)

Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks. - Ekaterina I. Tolstaya; James Paulos; Vijay Kumar 001; Alejandro Ribeiro (IROS, 2021)

Synthesizing Decentralized Controllers With Graph Neural Networks and Imitation Learning. - Fernando Gama; Qingbiao Li; Ekaterina I. Tolstaya; Amanda Prorok; Alejandro Ribeiro (IEEE Trans. Signal Process., 2022)