31 citations · 77 across the 20 of their papers we have counts for
4 papers · 1 filter
Interpretable Reinforcement Learning for Robotics and Continuous Control
Rohan Paleja, Letian Chen, Yaru Niu +10
Interpretability in machine learning is critical for the safe deployment of learned policies across legally-regulated and safety-critical domains. While gradient-based approaches i…
Learning Models of Adversarial Agent Behavior under Partial Observability
Sean Ye, Manisha Natarajan, Zixuan Wu +3
The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction. In this work,…
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment
Zixuan Wu, Sean Ye, Manisha Natarajan +3
We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial, evasive agent. The heterogeneous search team may only h…
The Effect of Robot Skill Level and Communication in Rapid, Proximate Human-Robot Collaboration
Kin Man Lee, Arjun Krishna, Zulfiqar Zaidi +5
As high-speed, agile robots become more commonplace, these robots will have the potential to better aid and collaborate with humans. However, due to the increased agility and funct…