Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories

16 October 2018

Papers citing "Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories"

9 / 9 papers shown

Title
An Algorithmic Perspective on Imitation Learning Takayuki Osa Joni Pajarinen Gerhard Neumann J. Andrew Bagnell Pieter Abbeel Jan Peters 90 848 0 16 Nov 2018
Generative Adversarial Imitation Learning Jonathan Ho Stefano Ermon GAN 165 812 0 10 Jun 2016
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation Adam Paszke Abhishek Chaurasia Sangpil Kim Eugenio Culurciello SSeg 328 2,084 0 07 Jun 2016
Fully Convolutional Networks for Semantic Segmentation Evan Shelhamer Jonathan Long Trevor Darrell VOS SSeg 760 37,927 0 20 May 2016
End to End Learning for Self-Driving Cars Mariusz Bojarski D. Testa Daniel Dworakowski Bernhard Firner B. Flepp ... Urs Muller Jiakai Zhang Xin Zhang Jake Zhao Karol Zieba SSL 102 4,179 0 25 Apr 2016
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization Chelsea Finn Sergey Levine Pieter Abbeel 112 952 0 01 Mar 2016
Multi-Scale Context Aggregation by Dilated Convolutions Feng Yu V. Koltun SSeg 274 8,464 0 23 Nov 2015
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badrinarayanan Alex Kendall R. Cipolla SSeg 1.1K 15,846 0 02 Nov 2015
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning Stéphane Ross Geoffrey J. Gordon J. Andrew Bagnell OffRL 263 3,238 0 02 Nov 2010