2 citations · 4 across the 5 of their papers we have counts for
4 papers · 1 filter
On the Complexity of Learning to Cooperate with Populations of Socially Rational Agents
Robert Loftin, Saptarashmi Bandyopadhyay, Mustafa Mert Çelikok
Artificially intelligent agents deployed in the real-world will require the ability to reliably \textit{cooperate} with humans (as well as other, heterogeneous AI agents). To provi…
Inverse Concave-Utility Reinforcement Learning is Inverse Game Theory
Mustafa Mert Çelikok, Frans A. Oliehoek, Jan-Willem van de Meent
We consider inverse reinforcement learning problems with concave utilities. Concave Utility Reinforcement Learning (CURL) is a generalisation of the standard RL objective, which em…
Teaching to Learn: Sequential Teaching of Agents with Inner States
Mustafa Mert Celikok, Pierre-Alexandre Murena, Samuel Kaski
In sequential machine teaching, a teacher's objective is to provide the optimal sequence of inputs to sequential learners in order to guide them towards the best model. In this pap…
Machine Teaching of Active Sequential Learners
Tomi Peltola, Mustafa Mert Çelikok, Pedram Daee +1
Machine teaching addresses the problem of finding the best training data that can guide a learning algorithm to a target model with minimal effort. In conventional settings, a teac…