9 citations · 20 across the 12 of their papers we have counts for
20 papers
Towards an Interpretable Hierarchical Agent Framework using Semantic Goals
Bharat Prakash, Nicholas Waytowich, Tim Oates +1
Learning to solve long horizon temporally extended tasks with reinforcement learning has been a challenge for several years now. We believe that it is important to leverage both th…
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…
Automatic Goal Generation using Dynamical Distance Learning
Bharat Prakash, Nicholas Waytowich, Tinoosh Mohsenin +1
Reinforcement Learning (RL) agents can learn to solve complex sequential decision making tasks by interacting with the environment. However, sample efficiency remains a major chall…
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…
Interactive Hierarchical Guidance using Language
Bharat Prakash, Nicholas Waytowich, Tim Oates +1
Reinforcement learning has been successful in many tasks ranging from robotic control, games, energy management etc. In complex real world environments with sparse rewards and long…
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.…