9 citations · 13 across the 9 of their papers we have counts for
10 papers
What Information Buys in Active Inference
Melih Kandemir, Mustafa Mert Çelikok
Information-theoretic exploration is of primary interest in methodological studies of reinforcement learning (RL) and computational cognitive neuroscience, while the established RL…
Social Cooperation in Conversational AI Agents
Mustafa Mert Çelikok, Saptarashmi Bandyopadhyay, Robert Loftin
The development of AI agents based on large, open-domain language models (LLMs) has paved the way for the development of general-purpose AI assistants that can support human in tas…
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…
Towards a Unifying Model of Rationality in Multiagent Systems
Robert Loftin, Mustafa Mert Çelikok, Frans A. Oliehoek
Multiagent systems deployed in the real world need to cooperate with other agents (including humans) nearly as effectively as these agents cooperate with one another. To design suc…
Uncoupled Learning of Differential Stackelberg Equilibria with Commitments
Robert Loftin, Mustafa Mert Çelikok, Herke van Hoof +2
In multi-agent problems requiring a high degree of cooperation, success often depends on the ability of the agents to adapt to each other's behavior. A natural solution concept in…