activity
20192022
most citedSMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

103 citations · 155 across the 18 of their papers we have counts for

collaborators
Showing 2021Show all

7 papers · 1 filter

cs.RO2021

Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning

Kasra Rezaee, Peyman Yadmellat, Masoud S. Nosrati +3

Competent multi-lane cruising requires using lane changes and within-lane maneuvers to achieve good speed and maintain safety. This paper proposes a design for autonomous multi-lan…

cs.AI202110 cited

Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment

Tianze Zhou, Fubiao Zhang, Kun Shao +10

Extending transfer learning to cooperative multi-agent reinforcement learning (MARL) has recently received much attention. In contrast to the single-agent setting, the coordination…

cs.CV20212 cited

PURE: Passive mUlti-peRson idEntification via Deep Footstep Separation and Recognition

Chao Cai, Ruinan Jin, Peng Wang +3

Recently, \textit{passive behavioral biometrics} (e.g., gesture or footstep) have become promising complements to conventional user identification methods (e.g., face or fingerprin…

cs.CV2021

Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map

Elmira Amirloo, Mohsen Rohani, Ershad Banijamali +2

While supervised learning is widely used for perception modules in conventional autonomous driving solutions, scalability is hindered by the huge amount of data labeling needed. In…

cs.RO2021

Learning robust driving policies without online exploration

Daniel Graves, Nhat M. Nguyen, Kimia Hassanzadeh +2

We propose a multi-time-scale predictive representation learning method to efficiently learn robust driving policies in an offline manner that generalize well to novel road geometr…

cs.RO2021

Open-set Intersection Intention Prediction for Autonomous Driving

Fei Li, Xiangxu Li, Jun Luo +2

Intention prediction is a crucial task for Autonomous Driving (AD). Due to the variety of size and layout of intersections, it is challenging to predict intention of human driver a…