2 citations · 6 across the 11 of their papers we have counts for
11 papers
Multi-Task Reward Learning from Human Ratings
Mingkang Wu, Devin White, Evelyn Rose +3
Reinforcement learning from human feedback (RLHF) has become a key factor in aligning model behavior with users' goals. However, while humans integrate multiple strategies when mak…
Adversarial Attacks on Reinforcement Learning Agents for Command and Control
Ahaan Dabholkar, James Z. Hare, Mark Mittrick +4
Given the recent impact of Deep Reinforcement Learning in training agents to win complex games like StarCraft and DoTA(Defense Of The Ancients) - there has been a surge in research…
Scalable Interactive Machine Learning for Future Command and Control
Anna Madison, Ellen Novoseller, Vinicius G. Goecks +7
Future warfare will require Command and Control (C2) personnel to make decisions at shrinking timescales in complex and potentially ill-defined situations. Given the need for robus…
COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations
Vinicius G. Goecks, Nicholas Waytowich
The development of Courses of Action (COAs) in military operations is traditionally a time-consuming and intricate process. Addressing this challenge, this study introduces COA-GPT…
StarCraftImage: A Dataset For Prototyping Spatial Reasoning Methods For Multi-Agent Environments
Sean Kulinski, Nicholas R. Waytowich, James Z. Hare +1
Spatial reasoning tasks in multi-agent environments such as event prediction, agent type identification, or missing data imputation are important for multiple applications (e.g., a…
DIP-RL: Demonstration-Inferred Preference Learning in Minecraft
Ellen Novoseller, Vinicius G. Goecks, David Watkins +2
In machine learning for sequential decision-making, an algorithmic agent learns to interact with an environment while receiving feedback in the form of a reward signal. However, in…