Sample complexity

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization

Chelsea Finn, Sergey Levine, Pieter Abbeel

Citations: 370 • 2016

Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations

Mark G. Pfeiffer, Samarth Shukla, Matteo Turchetta, César Cadena, Andreas Krause, Roland Siegwart, Juan Nieto

Citations: 198 • 2018

A Review of Safe Reinforcement Learning: Methods, Theories, and Applications

Shangding Gu, Yang Long, Yali Du, Guang Chen, Florian Walter, Jun Wang, Alois Knoll

Citations: 194 • 2024

A Review of Safe Reinforcement Learning: Methods, Theory and Applications

Shangding Gu, Long Yang, Yali Du, Guang Chen, Florian Walter, Jun Wang, Alois Knoll

Citations: 102 • 2022

The adaptive <i>k</i> -meteorologists problem and its application to structure learning and feature selection in reinforcement learning

Carlos Diuk, Lihong Li, Bethany R. Leffler

Citations: 80 • 2009

Wasserstein Adversarial Imitation Learning

Huang Xiao, Michael Herman, Sebastian Ziesche, Jalal Etesami, Thai Hong Linh

Citations: 35 • 2019

Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing

Axel Brunnbauer, Luigi Berducci, Andreas Brandstätter, Mathias Lechner, Ramin Hasani, Daniela Rus, Radu Grosu

Citations: 35 • 2022

Dimension reduction and its application to model-based exploration in continuous spaces

Ali Nouri, Michael L. Littman

Citations: 30 • 2010

Faster Sample-Based Motion Planning Using Instance-Based Learning

Jia Pan, Sachin Chitta, Dinesh Manocha

Citations: 30 • 2013

MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models

Daniël Willemsen, Mario Coppola, Guido de Croon

Citations: 22 • 2021

Provably Efficient Learning with Typed Parametric Models

Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy

Citations: 19 • 2009

Offline-to-Online Reinforcement Learning via Balanced Replay and\n Pessimistic Q-Ensemble

Seunghyun Lee, Younggyo Seo, Kimin Lee, Pieter Abbeel, Jinwoo Shin

Citations: 18 • 2021

Jump-Start Reinforcement Learning

Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, Sergey Levine, Karol Hausman

Citations: 17 • 2022

Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrations

Mark G. Pfeiffer, Samarth Shukla, Matteo Turchetta, César Cadena, Andreas Krause, Roland Siegwart, Juan Nieto

Citations: 16 • 2018

Cyclic policy distillation: Sample-efficient sim-to-real reinforcement learning with domain randomization

Yuki Kadokawa, Lingwei Zhu, Yoshihisa Tsurumine, Takamitsu Matsubara

Citations: 16 • 2023

Real-Time Scheduling via Reinforcement Learning

Robert Glaubius, Terry Tidwell, Christopher Gill, William D. Smart

Citations: 15 • 2012

Localization vs. Identification of Semi-Algebraic Sets

Shai Ben-David, Michael Lindenbaum

Citations: 15 • 1998

Complexity Issues in Robotic Machine Learning of Natural Language

Patrick Suppes, Michale Böttner

Citations: 14 • 1992

An OCBA-Based Method for Efficient Sample Collection in Reinforcement Learning

Kuo Li, Xinze Jin, Qing‐Shan Jia, Dongchun Ren, Huaxia Xia

Citations: 14 • 2023

Model-Based Deep Reinforcement Learning for High-Dimensional Problems, a Survey.

Aske Plaat, Walter A. Kosters, Mike Preuß

Citations: 12 • 2020