Andrew Patterson

activity

20192021

most citedGradient Temporal-Difference Learning with Regularized Corrections

9 citations · 16 across the 7 of their papers we have counts for

collaborators

Showing 2020Show all

4 papers · 1 filter

eess.SY2020

Contraction $L_{1}$ -Adaptive Control using Gaussian Processes

Aditya Gahlawat, Arun Lakshmanan, Lin Song +4

We present $CL_{1}$ - $G P$ , a control framework that enables safe simultaneous learning and control for systems subject to uncertainties. The two main constituents…

cs.LG2020★ 9 cited

Gradient Temporal-Difference Learning with Regularized Corrections

Sina Ghiassian, Andrew Patterson, Shivam Garg +3

It is still common to use Q-learning and temporal difference (TD) learning-even though they have divergence issues and sound Gradient TD alternatives exist-because divergence seems…

eess.SY2020

$L_{1}$ - $G P$ : $L_{1}$ Adaptive Control with Bayesian Learning

Aditya Gahlawat, Pan Zhao, Andrew Patterson +2

We present $L_{1}$ - $G P$ , an architecture based on $L_{1}$ adaptive control and Gaussian Process Regression (GPR) for safe simultaneous control and learnin…

cs.RO2020

Learning Probabilistic Intersection Traffic Models for Trajectory Prediction

Andrew Patterson, Aditya Gahlawat, Naira Hovakimyan

Autonomous agents must be able to safely interact with other vehicles to integrate into urban environments. The safety of these agents is dependent on their ability to predict coll…

Contraction L1​-Adaptive Control using Gaussian Processes

Gradient Temporal-Difference Learning with Regularized Corrections

L1​-GP: L1​ Adaptive Control with Bayesian Learning

Learning Probabilistic Intersection Traffic Models for Trajectory Prediction

Contraction $L_{1}$ -Adaptive Control using Gaussian Processes

$L_{1}$ - $G P$ : $L_{1}$ Adaptive Control with Bayesian Learning