9 citations · 16 across the 7 of their papers we have counts for
7 papers
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning
Andrew Patterson, Adam White, Martha White
Many reinforcement learning algorithms rely on value estimation, however, the most widely used algorithms -- namely temporal difference algorithms -- can diverge under both off-pol…
Contraction -Adaptive Control using Gaussian Processes
Aditya Gahlawat, Arun Lakshmanan, Lin Song +4
We present -, a control framework that enables safe simultaneous learning and control for systems subject to uncertainties. The two main constituents…
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…
-: Adaptive Control with Bayesian Learning
Aditya Gahlawat, Pan Zhao, Andrew Patterson +2
We present -, an architecture based on adaptive control and Gaussian Process Regression (GPR) for safe simultaneous control and learnin…
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…
Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification
Andrew Patterson, Arun Lakshmanan, Naira Hovakimyan
Collision prediction in a dynamic and unknown environment relies on knowledge of how the environment is changing. Many collision prediction methods rely on deterministic knowledge…