Search Results for author: Derrik E. Asher

Found 6 papers, 0 papers with code

Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II

no code implementations11 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.

reinforcement-learning Starcraft +1

Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination

no code implementations17 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.


On games and simulators as a platform for development of artificial intelligence for command and control

no code implementations21 Oct 2021 Vinicius G. Goecks, Nicholas Waytowich, Derrik E. Asher, Song Jun Park, Mark Mittrick, John Richardson, Manuel Vindiola, Anne Logie, Mark Dennison, Theron Trout, Priya Narayanan, Alexander Kott

Games and simulators can be a valuable platform to execute complex multi-agent, multiplayer, imperfect information scenarios with significant parallels to military applications: multiple participants manage resources and make decisions that command assets to secure specific areas of a map or neutralize opposing forces.

Starcraft Starcraft II

Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training

no code implementations29 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

On Memory Mechanism in Multi-Agent Reinforcement Learning

no code implementations11 Sep 2019 Yilun Zhou, Derrik E. Asher, Nicholas R. Waytowich, Julie A. Shah

Multi-agent reinforcement learning (MARL) extends (single-agent) reinforcement learning (RL) by introducing additional agents and (potentially) partial observability of the environment.

Multi-agent Reinforcement Learning reinforcement-learning

Coordination-driven learning in multi-agent problem spaces

no code implementations13 Sep 2018 Sean L. Barton, Nicholas R. Waytowich, Derrik E. Asher

We discuss the role of coordination as a direct learning objective in multi-agent reinforcement learning (MARL) domains.

Multi-agent Reinforcement Learning reinforcement-learning

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