5 citations · 9 across the 5 of their papers we have counts for
5 papers
A Narration-based Reward Shaping Approach using Grounded Natural Language Commands
Nicholas Waytowich, Sean L. Barton, Vernon Lawhern +1
While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniq…
Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning
Nicholas Waytowich, Sean L. Barton, Vernon Lawhern +2
While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniq…
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…