10 citations · 18 across the 11 of their papers we have counts for
3 papers · 1 filter
Robust and Diverse Multi-Agent Learning via Rational Policy Gradient
Niklas Lauffer, Ameesh Shah, Micah Carroll +3
Adversarial optimization algorithms that explicitly search for flaws in agents' policies have been successfully applied to finding robust and diverse policies in multi-agent settin…
Who Needs to Know? Minimal Knowledge for Optimal Coordination
Niklas Lauffer, Ameesh Shah, Micah Carroll +2
To optimally coordinate with others in cooperative games, it is often crucial to have information about one's collaborators: successful driving requires understanding which side of…
Demonstration Informed Specification Search
Marcell Vazquez-Chanlatte, Ameesh Shah, Gil Lederman +1
This paper considers the problem of learning temporal task specifications, e.g. automata and temporal logic, from expert demonstrations. Task specifications are a class of sparse m…