103 citations · 355 across the 24 of their papers we have counts for
5 papers · 1 filter
Learning to Teach in Cooperative Multiagent Reinforcement Learning
Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu +5
Collective human knowledge has clearly benefited from the fact that innovations by individuals are taught to others through communication. Similar to human social groups, agents in…
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…
Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning
Yu Fan Chen, Miao Liu, Michael Everett +1
Finding feasible, collision-free paths for multiagent systems can be challenging, particularly in non-communicating scenarios where each agent's intent (e.g. goal) is unobservable…