Publications (5)
Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training
Piyush K. Sharma, Rolando Fernandez, Erin Zaroukian +3
Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) appro…
Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II
Nicholas Waytowich, James Hare, Vinicius G. Goecks +4
Traditionally, learning from human demonstrations via direct behavior cloning can lead to high-performance policies given that the algorithm has access to large amounts of high-qua…
Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination
Derrik E. Asher, Anjon Basak, Rolando Fernandez +9
Reinforcement learning (RL) approaches can illuminate emergent behaviors that facilitate coordination across teams of agents as part of a multi-agent system (MAS), which can provid…
SERN: Bandwidth-Adaptive Cross-Reality Synchronization for Simulation-Enhanced Robot Navigation
Jumman Hossain, Emon Dey, Snehalraj Chugh +15
Cross reality integration of simulation and physical robots is a promising approach for multi-robot operations in contested environments, where communication may be intermittent, i…
Learning Multi-Robot Coordination through Locality-Based Factorized Multi-Agent Actor-Critic Algorithm
Chak Lam Shek, Amrit Singh Bedi, Anjon Basak +5
In this work, we present a novel cooperative multi-agent reinforcement learning method called \textbf{Loc}ality based \textbf{Fac}torized \textbf{M}ulti-Agent \textbf{A}ctor-\textb…