no code implementations • 14 Feb 2024 • Allen M. Wang, Oswin So, Charles Dawson, Darren T. Garnier, Cristina Rea, Chuchu Fan
The policy training environment is a hybrid physics and machine learning model trained on simulations of the SPARC primary reference discharge (PRD) ramp-down, an upcoming burning plasma scenario which we use as a testbed.
no code implementations • 28 May 2023 • Justin Lidard, Oswin So, Yanxia Zhang, Jonathan DeCastro, Xiongyi Cui, Xin Huang, Yen-Ling Kuo, John Leonard, Avinash Balachandran, Naomi Leonard, Guy Rosman
Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents.
no code implementations • 30 Sep 2022 • Oswin So, Gongjie Li, Evangelos A. Theodorou, Molei Tao
Incorporating the Hamiltonian structure of physical dynamics into deep learning models provides a powerful way to improve the interpretability and prediction accuracy.
1 code implementation • 20 Sep 2022 • Guan-Horng Liu, Tianrong Chen, Oswin So, Evangelos A. Theodorou
In this work, we aim at solving a challenging class of MFGs in which the differentiability of these interacting preferences may not be available to the solver, and the population is urged to converge exactly to some desired distribution.
no code implementations • 22 Feb 2022 • Marcus A. Pereira, Augustinos D. Saravanos, Oswin So, Evangelos A. Theodorou
In this work, we propose a novel safe and scalable decentralized solution for multi-agent control in the presence of stochastic disturbances.
no code implementations • 1 Apr 2021 • Ziyi Wang, Oswin So, Jason Gibson, Bogdan Vlahov, Manan S. Gandhi, Guan-Horng Liu, Evangelos A. Theodorou
In this paper, we provide a generalized framework for Variational Inference-Stochastic Optimal Control by using thenon-extensive Tsallis divergence.
no code implementations • 2 Sep 2020 • Ziyi Wang, Oswin So, Keuntaek Lee, Camilo A. Duarte, Evangelos A. Theodorou
We present a general framework for optimizing the Conditional Value-at-Risk for dynamical systems using stochastic search.
Distributional Reinforcement Learning Optimization and Control Robotics