16 citations · 26 across the 13 of their papers we have counts for
13 papers
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson +1
Multi-agent reinforcement learning typically suffers from the problem of sample inefficiency, where learning suitable policies involves the use of many data samples. Learning from…
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning
Bram Grooten, Ghada Sokar, Shibhansh Dohare +4
Tomorrow's robots will need to distinguish useful information from noise when performing different tasks. A household robot for instance may continuously receive a plethora of info…
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…