8 citations · 34 across the 14 of their papers we have counts for
18 papers
Multi-Fidelity Policy Gradients Stabilize Data-Scarce Reinforcement Learning
Xinjie Liu, Ruihan Zhao, Anirban Chaudhuri +3
Policy gradient methods for on-policy reinforcement learning (RL) can become unstable when expensive, scarce target-domain data yield noisy gradient estimates. We address this chal…
A Multi-Fidelity Control Variate Approach for Policy Gradient Estimation
Xinjie Liu, Cyrus Neary, Kushagra Gupta +4
Many reinforcement learning (RL) algorithms are impractical for training in operational systems or computationally expensive high-fidelity simulations, as they require large amount…
Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks
Cyrus Neary, Nathan Tsao, Ufuk Topcu
We develop compositional learning algorithms for coupled dynamical systems, with a particular focus on electrical networks. While deep learning has proven effective at modeling com…
A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning
Cyrus Neary, Christian Ellis, Aryaman Singh Samyal +2
We propose and demonstrate a compositional framework for training and verifying reinforcement learning (RL) systems within a multifidelity sim-to-real pipeline, in order to deploy…
Formal Methods for Autonomous Systems
Tichakorn Wongpiromsarn, Mahsa Ghasemi, Murat Cubuktepe +6
Formal methods refer to rigorous, mathematical approaches to system development and have played a key role in establishing the correctness of safety-critical systems. The main buil…
Verifiable Reinforcement Learning Systems via Compositionality
Cyrus Neary, Aryaman Singh Samyal, Christos Verginis +2
We propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, a…
Multimodal Pretrained Models for Verifiable Sequential Decision-Making: Planning, Grounding, and Perception
Yunhao Yang, Cyrus Neary, Ufuk Topcu
Recently developed pretrained models can encode rich world knowledge expressed in multiple modalities, such as text and images. However, the outputs of these models cannot be integ…
How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations
Franck Djeumou, Cyrus Neary, Ufuk Topcu
We present a framework and algorithms to learn controlled dynamics models using neural stochastic differential equations (SDEs) -- SDEs whose drift and diffusion terms are both par…
Differential Privacy in Cooperative Multiagent Planning
Bo Chen, Calvin Hawkins, Mustafa O. Karabag +3
Privacy-aware multiagent systems must protect agents' sensitive data while simultaneously ensuring that agents accomplish their shared objectives. Towards this goal, we propose a f…
Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven Control
Adam J. Thorpe, Cyrus Neary, Franck Djeumou +2
Data-driven control algorithms use observations of system dynamics to construct an implicit model for the purpose of control. However, in practice, data-driven techniques often req…
Automaton-Based Representations of Task Knowledge from Generative Language Models
Yunhao Yang, Jean-Raphaël Gaglione, Cyrus Neary +1
Automaton-based representations of task knowledge play an important role in control and planning for sequential decision-making problems. However, obtaining the high-level task kno…
Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks
Cyrus Neary, Ufuk Topcu
Many dynamical systems -- from robots interacting with their surroundings to large-scale multiphysics systems -- involve a number of interacting subsystems. Toward the objective of…
Planning Not to Talk: Multiagent Systems that are Robust to Communication Loss
Mustafa O. Karabag, Cyrus Neary, Ufuk Topcu
In a cooperative multiagent system, a collection of agents executes a joint policy in order to achieve some common objective. The successful deployment of such systems hinges on th…
Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs
Franck Djeumou, Cyrus Neary, Eric Goubault +2
Neural ordinary differential equations (NODEs) -- parametrizations of differential equations using neural networks -- have shown tremendous promise in learning models of unknown co…
Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
Franck Djeumou, Cyrus Neary, Eric Goubault +2
Effective inclusion of physics-based knowledge into deep neural network models of dynamical systems can greatly improve data efficiency and generalization. Such a-priori knowledge…
Verifiable and Compositional Reinforcement Learning Systems
Cyrus Neary, Christos Verginis, Murat Cubuktepe +1
We propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, a…
Smooth Convex Optimization using Sub-Zeroth-Order Oracles
Mustafa O. Karabag, Cyrus Neary, Ufuk Topcu
We consider the problem of minimizing a smooth, Lipschitz, convex function over a compact, convex set using sub-zeroth-order oracles: an oracle that outputs the sign of the directi…
Reward Machines for Cooperative Multi-Agent Reinforcement Learning
Cyrus Neary, Zhe Xu, Bo Wu +1
In cooperative multi-agent reinforcement learning, a collection of agents learns to interact in a shared environment to achieve a common goal. We propose the use of reward machines…