103 citations · 158 across the 23 of their papers we have counts for
23 papers
NeurIPS 2022 Competition: Driving SMARTS
Amir Rasouli, Randy Goebel, Matthew E. Taylor +15
Driving SMARTS is a regular competition designed to tackle problems caused by the distribution shift in dynamic interaction contexts that are prevalent in real-world autonomous dri…
Auxiliary task discovery through generate-and-test
Banafsheh Rafiee, Sina Ghiassian, Jun Jin +3
In this paper, we explore an approach to auxiliary task discovery in reinforcement learning based on ideas from representation learning. Auxiliary tasks tend to improve data effici…
Reinforcement Teaching
Calarina Muslimani, Alex Lewandowski, Dale Schuurmans +2
Machine learning algorithms learn to solve a task, but are unable to improve their ability to learn. Meta-learning methods learn about machine learning algorithms and improve them…
What makes useful auxiliary tasks in reinforcement learning: investigating the effect of the target policy
Banafsheh Rafiee, Jun Jin, Jun Luo +1
Auxiliary tasks have been argued to be useful for representation learning in reinforcement learning. Although many auxiliary tasks have been empirically shown to be effective for a…
LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction
Elmira Amirloo, Amir Rasouli, Peter Lakner +2
Multi-agent trajectory prediction is a fundamental problem in autonomous driving. The key challenges in prediction are accurately anticipating the behavior of surrounding agents an…
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…