most citedSafe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces

10 citations · 10 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2021★ 10 cited

Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces

Ziyad Sheebaelhamd, Konstantinos Zisis, Athina Nisioti +3

Multi-agent control problems constitute an interesting area of application for deep reinforcement learning models with continuous action spaces. Such real-world applications, howev…

math.OC2021

Decentralized trajectory optimization for multi-agent exploration

Dimitris Gkouletsos, Andrea Iannelli, Mathias Hudoba de Badyn +1

Autonomous exploration is an application of growing importance in robotics. A promising strategy is ergodic trajectory planning, whereby an agent spends in each area a fraction of…