33 citations · 41 across the 6 of their papers we have counts for
7 papers
Multi-Agent Synchronization Tasks
Rolando Fernandez, Garrett Warnell, Derrik E. Asher +1
In multi-agent reinforcement learning (MARL), coordination plays a crucial role in enhancing agents' performance beyond what they could achieve through cooperation alone. The inter…
CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning
Jumman Hossain, Abu-Zaher Faridee, Nirmalya Roy +2
Autonomous navigation in offroad environments has been extensively studied in the robotics field. However, navigation in covert situations where an autonomous vehicle needs to rema…
Emergent Behaviors in Multi-Agent Target Acquisition
Piyush K. Sharma, Erin Zaroukian, Derrik E. Asher +1
Only limited studies and superficial evaluations are available on agents' behaviors and roles within a Multi-Agent System (MAS). We simulate a MAS using Reinforcement Learning (RL)…
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…
On games and simulators as a platform for development of artificial intelligence for command and control
Vinicius G. Goecks, Nicholas Waytowich, Derrik E. Asher +9
Games and simulators can be a valuable platform to execute complex multi-agent, multiplayer, imperfect information scenarios with significant parallels to military applications: mu…
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…