5 citations · 10 across the 8 of their papers we have counts for
8 papers
On Memory Mechanism in Multi-Agent Reinforcement Learning
Yilun Zhou, Derrik E. Asher, Nicholas R. Waytowich +1
Multi-agent reinforcement learning (MARL) extends (single-agent) reinforcement learning (RL) by introducing additional agents and (potentially) partial observability of the environ…
Inferring and Learning Multi-Robot Policies by Observing an Expert
Pietro Pierpaoli, Harish Ravichandar, Nicholas Waytowich +3
We present a technique for learning how to solve a multi-robot mission that requires interaction with an external environment by observing an expert system executing the same missi…
Opinion Formation Threshold Estimates from Different Combinations of Social Media Data-Types
Derrik E. Asher, Justine Caylor, Casey Doyle +3
Passive consumption of a quantifiable amount of social media information related to a topic can cause individuals to form opinions. If a substantial amount of these individuals are…
Coordination-driven learning in multi-agent problem spaces
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. To this end, we present a novel means of quantifying coordi…
Measuring collaborative emergent behavior in multi-agent reinforcement learning
Sean L. Barton, Nicholas R. Waytowich, Erin Zaroukian +1
Multi-agent reinforcement learning (RL) has important implications for the future of human-agent teaming. We show that improved performance with multi-agent RL is not a guarantee o…
Adapting the Predator-Prey Game Theoretic Environment to Army Tactical Edge Scenarios with Computational Multiagent Systems
Derrik E. Asher, Erin Zaroukian, Sean L. Barton
The historical origins of the game theoretic predator-prey pursuit problem can be traced back to Benda, et al., 1985 [1]. Their work adapted the predator-prey ecology problem into…