59 citations · 59 across the 4 of their papers we have counts for
4 papers
In Trust We Survive: Emergent Trust Learning
Qianpu Chen, Giulio Barbero, Mike Preuss +1
We introduce Emergent Trust Learning (ETL), a lightweight, trust-based control algorithm that can be plugged into existing AI agents. It enables these to reach cooperation in compe…
Guiding Skill Discovery with Foundation Models
Zhao Yang, Thomas M. Moerland, Mike Preuss +3
Learning diverse skills without hand-crafted reward functions could accelerate reinforcement learning in downstream tasks. However, existing skill discovery methods focus solely on…
Agentic Large Language Models, a survey
Aske Plaat, Max van Duijn, Niki van Stein +3
Background: There is great interest in agentic LLMs, large language models that act as agents. Objectives: We review the growing body of work in this area and provide a research ag…
Reset-free Reinforcement Learning with World Models
Zhao Yang, Thomas M. Moerland, Mike Preuss +2
Reinforcement learning (RL) is an appealing paradigm for training intelligent agents, enabling policy acquisition from the agent's own autonomously acquired experience. However, th…