no code implementations • 11 May 2022 • Nicholas Waytowich, James Hare, Vinicius G. Goecks, Mark Mittrick, John Richardson, Anjon Basak, Derrik E. Asher
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-quality data covering the most likely scenarios to be encountered when the agent is operating.
no code implementations • 17 Mar 2022 • Derrik E. Asher, Anjon Basak, Rolando Fernandez, Piyush K. Sharma, Erin G. Zaroukian, Christopher D. Hsu, Michael R. Dorothy, Thomas Mahre, Gerardo Galindo, Luke Frerichs, John Rogers, John Fossaceca
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 provide windows of opportunity in various military tasks.
no code implementations • 29 Jul 2021 • Piyush K. Sharma, Rolando Fernandez, Erin Zaroukian, Michael Dorothy, Anjon Basak, Derrik E. Asher
Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) approach to achieve human-like collaboration in cooperative tasks.
Multi-agent Reinforcement Learning reinforcement-learning +1