16 citations · 26 across the 13 of their papers we have counts for
5 papers · 1 filter
Safe Evaluation For Offline Learning: Are We Ready To Deploy?
Hager Radi, Josiah P. Hanna, Peter Stone +1
The world currently offers an abundance of data in multiple domains, from which we can learn reinforcement learning (RL) policies without further interaction with the environment.…
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…
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction
Taher Jafferjee, Juliusz Ziomek, Tianpei Yang +6
Centralised training with decentralised execution (CT-DE) serves as the foundation of many leading multi-agent reinforcement learning (MARL) algorithms. Despite its popularity, it…
Methodical Advice Collection and Reuse in Deep Reinforcement Learning
Sahir, Ercüment İlhan, Srijita Das +1
Reinforcement learning (RL) has shown great success in solving many challenging tasks via use of deep neural networks. Although using deep learning for RL brings immense representa…
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…