activity
20192021
most citedSMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

103 citations · 128 across the 13 of their papers we have counts for

collaborators

13 papers

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…

cs.LG2021

CoachNet: An Adversarial Sampling Approach for Reinforcement Learning

Elmira Amirloo Abolfathi, Jun Luo, Peyman Yadmellat +1

Despite the recent successes of reinforcement learning in games and robotics, it is yet to become broadly practical. Sample efficiency and unreliable performance in rare but challe…

cs.LG20202 cited

LISPR: An Options Framework for Policy Reuse with Reinforcement Learning

Daniel Graves, Jun Jin, Jun Luo

We propose a framework for transferring any existing policy from a potentially unknown source MDP to a target MDP. This framework (1) enables reuse in the target domain of any form…