64 citations · 101 across the 31 of their papers we have counts for
11 papers · 1 filter
Safe adaptation in multiagent competition
Macheng Shen, Jonathan P. How
Achieving the capability of adapting to ever-changing environments is a critical step towards building fully autonomous robots that operate safely in complicated scenarios. In mult…
ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation
Chuangchuang Sun, Dong-Ki Kim, Jonathan P. How
In a multirobot system, a number of cyber-physical attacks (e.g., communication hijack, observation perturbations) can challenge the robustness of agents. This robustness issue wor…
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
Dong-Ki Kim, Miao Liu, Matthew Riemer +6
A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents. In particular, each…
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…
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…
Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning
Arpan Kusari, Jonathan P. How
A common approach for defining a reward function for Multi-objective Reinforcement Learning (MORL) problems is the weighted sum of the multiple objectives. The weights are then tre…