240 citations · 364 across the 22 of their papers we have counts for
4 papers · 1 filter
Influencing Long-Term Behavior in Multiagent Reinforcement Learning
Dong-Ki Kim, Matthew Riemer, Miao Liu +5
The main challenge of multiagent reinforcement learning is the difficulty of learning useful policies in the presence of other simultaneously learning agents whose changing behavio…
Robustness Analysis of Neural Networks via Efficient Partitioning with Applications in Control Systems
Michael Everett, Golnaz Habibi, Jonathan P. How
Neural networks (NNs) are now routinely implemented on systems that must operate in uncertain environments, but the tools for formally analyzing how this uncertainty propagates to…
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning
Michael Everett, Bjorn Lutjens, Jonathan P. How
Deep Neural Network-based systems are now the state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees…
Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning
Samaneh Hosseini Semnani, Hugh Liu, Michael Everett +2
This paper introduces a hybrid algorithm of deep reinforcement learning (RL) and Force-based motion planning (FMP) to solve distributed motion planning problem in dense and dynamic…