Search Results for author: Kendall Lowrey

Found 10 papers, 4 papers with code

BAM: Bayes with Adaptive Memory

no code implementations4 Feb 2022 Josue Nassar, Jennifer Brennan, Ben Evans, Kendall Lowrey

Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs.

online learning

BAM: Bayes Augmented with Memory

no code implementations ICLR 2022 Josue Nassar, Jennifer Rogers Brennan, Ben Evans, Kendall Lowrey

Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs.

online learning

Koopman Spectrum Nonlinear Regulator and Provably Efficient Online Learning

1 code implementation30 Jun 2021 Motoya Ohnishi, Isao Ishikawa, Kendall Lowrey, Masahiro Ikeda, Sham Kakade, Yoshinobu Kawahara

In this work, we present a novel paradigm of controlling nonlinear systems via the minimization of the Koopman spectrum cost: a cost over the Koopman operator of the controlled dynamics.

online learning reinforcement-learning

Faster Policy Learning with Continuous-Time Gradients

3 code implementations12 Dec 2020 Samuel Ainsworth, Kendall Lowrey, John Thickstun, Zaid Harchaoui, Siddhartha Srinivasa

We study the estimation of policy gradients for continuous-time systems with known dynamics.

Information Theoretic Regret Bounds for Online Nonlinear Control

1 code implementation NeurIPS 2020 Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun

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 Reproducing Kernel Hilbert Space.

Continuous Control

Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control

no code implementations ICLR 2019 Kendall Lowrey, Aravind Rajeswaran, Sham Kakade, Emanuel Todorov, Igor Mordatch

We study how local trajectory optimization can cope with approximation errors in the value function, and can stabilize and accelerate value function learning.

Towards Generalization and Simplicity in Continuous Control

1 code implementation NeurIPS 2017 Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, Sham Kakade

This work shows that policies with simple linear and RBF parameterizations can be trained to solve a variety of continuous control tasks, including the OpenAI gym benchmarks.

Continuous Control OpenAI Gym

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