16 citations · 52 across the 20 of their papers we have counts for
10 papers · 1 filter
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…
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…
DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarios
Vinicius G. Goecks, Nicholas R. Waytowich
The development of plans of action in disaster response scenarios is a time-consuming process. Large Language Models (LLMs) offer a powerful solution to expedite this process throu…
Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II
Nicholas Waytowich, James Hare, Vinicius G. Goecks +4
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-qua…
Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft
Vinicius G. Goecks, Nicholas Waytowich, David Watkins-Valls +1
Real-world tasks of interest are generally poorly defined by human-readable descriptions and have no pre-defined reward signals unless it is defined by a human designer. Conversely…
On games and simulators as a platform for development of artificial intelligence for command and control
Vinicius G. Goecks, Nicholas Waytowich, Derrik E. Asher +9
Games and simulators can be a valuable platform to execute complex multi-agent, multiplayer, imperfect information scenarios with significant parallels to military applications: mu…
Imitation Learning with Human Eye Gaze via Multi-Objective Prediction
Ravi Kumar Thakur, MD-Nazmus Samin Sunbeam, Vinicius G. Goecks +6
Approaches for teaching learning agents via human demonstrations have been widely studied and successfully applied to multiple domains. However, the majority of imitation learning…
Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems
Vinicius G. Goecks
Recent successes combine reinforcement learning algorithms and deep neural networks, despite reinforcement learning not being widely applied to robotics and real world scenarios. T…
PODNet: A Neural Network for Discovery of Plannable Options
Ritwik Bera, Vinicius G. Goecks, Gregory M. Gremillion +2
Learning from demonstration has been widely studied in machine learning but becomes challenging when the demonstrated trajectories are unstructured and follow different objectives.…
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments
Vinicius G. Goecks, Gregory M. Gremillion, Vernon J. Lawhern +2
This paper investigates how to efficiently transition and update policies, trained initially with demonstrations, using off-policy actor-critic reinforcement learning. It is well-k…