180 citations · 601 across the 46 of their papers we have counts for
5 papers · 1 filter
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems
Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar +2
A key challenge in multi-robot and multi-agent systems is generating solutions that are robust to other self-interested or even adversarial parties who actively try to prevent the…
Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions
Miao Liu, Kavinayan Sivakumar, Shayegan Omidshafiei +2
This paper presents a data-driven approach for multi-robot coordination in partially-observable domains based on Decentralized Partially Observable Markov Decision Processes (Dec-P…
Scalable Accelerated Decentralized Multi-Robot Policy Search in Continuous Observation Spaces
Shayegan Omidshafiei, Christopher Amato, Miao Liu +3
This paper presents the first ever approach for solving \emph{continuous-observation} Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) and their semi-Marko…
Semantic-level Decentralized Multi-Robot Decision-Making using Probabilistic Macro-Observations
Shayegan Omidshafiei, Shih-Yuan Liu, Michael Everett +5
Robust environment perception is essential for decision-making on robots operating in complex domains. Intelligent task execution requires principled treatment of uncertainty sourc…
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability
Shayegan Omidshafiei, Jason Pazis, Christopher Amato +2
Many real-world tasks involve multiple agents with partial observability and limited communication. Learning is challenging in these settings due to local viewpoints of agents, whi…