64 citations · 101 across the 31 of their papers we have counts for
6 papers · 1 filter
Scaling Up Multiagent Reinforcement Learning for Robotic Systems: Learn an Adaptive Sparse Communication Graph
Chuangchuang Sun, Macheng Shen, Jonathan P. How
The complexity of multiagent reinforcement learning (MARL) in multiagent systems increases exponentially with respect to the agent number. This scalability issue prevents MARL from…
R-MADDPG for Partially Observable Environments and Limited Communication
Rose E. Wang, Michael Everett, Jonathan P. How
There are several real-world tasks that would benefit from applying multiagent reinforcement learning (MARL) algorithms, including the coordination among self-driving cars. The rea…
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…