Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of
Gaussian Processes

Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

19 June 2020

Ding Zhao

Papers citing "Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes"

7 / 7 papers shown

Title
Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review Chengmin Zhou Chao Wang Haseeb Hassan H. Shah Bingding Huang P. Fränti 3DV 25 3 0 16 Jul 2023
Online Reinforcement Learning in Non-Stationary Context-Driven Environments Pouya Hamadanian Arash Nasr-Esfahany Malte Schwarzkopf Siddartha Sen MohammadIman Alizadeh CLL OffRL 40 0 0 04 Feb 2023
Building a Subspace of Policies for Scalable Continual Learning Jean-Baptiste Gaya T. Doan Lucas Page-Caccia Laure Soulier Ludovic Denoyer Roberta Raileanu CLL 16 29 0 18 Nov 2022
Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability Mengdi Xu Zuxin Liu Peide Huang Wenhao Ding Zhepeng Cen Bo-wen Li Ding Zhao 61 45 0 16 Sep 2022
Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training Peide Huang Mengdi Xu Fei Fang Ding Zhao 56 37 0 19 Feb 2022
$Improving the Robustness of Reinforcement Learning Policies with $\mathcal{L}_{1}$ Adaptive Control$ Improving the Robustness of Reinforcement Learning Policies with $\mathcal{L}_{1}$ Adaptive Control Y. Cheng Penghui Zhao F. Wang D. Block N. Hovakimyan 31 8 0 03 Dec 2021
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 243 11,568 0 09 Mar 2017