32 citations · 63 across the 4 of their papers we have counts for
6 papers
BAM: Bayes with Adaptive Memory
Josue Nassar, Jennifer Brennan, Ben Evans +1
Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs. However, a naive application of Bayesian methods in non stationary…
Faster Policy Learning with Continuous-Time Gradients
Samuel Ainsworth, Kendall Lowrey, John Thickstun +2
We study the estimation of policy gradients for continuous-time systems with known dynamics. By reframing policy learning in continuous-time, we show that it is possible construct…
Information Theoretic Regret Bounds for Online Nonlinear Control
Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey +2
This work studies the problem of sequential control in an unknown, nonlinear dynamical system, where we model the underlying system dynamics as an unknown function in a known Repro…
Model-Based Generalization Under Parameter Uncertainty Using Path Integral Control
Ian Abraham, Ankur Handa, Nathan Ratliff +3
This work addresses the problem of robot interaction in complex environments where online control and adaptation is necessary. By expanding the sample space in the free energy form…
Lyceum: An efficient and scalable ecosystem for robot learning
Colin Summers, Kendall Lowrey, Aravind Rajeswaran +2
We introduce Lyceum, a high-performance computational ecosystem for robot learning. Lyceum is built on top of the Julia programming language and the MuJoCo physics simulator, combi…
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
Kendall Lowrey, Aravind Rajeswaran, Sham Kakade +2
We propose a plan online and learn offline (POLO) framework for the setting where an agent, with an internal model, needs to continually act and learn in the world. Our work builds…