3 papers
cs.AI2025
Making Universal Policies Universal
Niklas Höpner, David Kuric, Herke van Hoof
The development of a generalist agent capable of solving a wide range of sequential decision-making tasks remains a significant challenge. We address this problem in a cross-agent…
cs.LG2024
Planning with a Learned Policy Basis to Optimally Solve Complex Tasks
Guillermo Infante, David Kuric, Anders Jonsson +2
Conventional reinforcement learning (RL) methods can successfully solve a wide range of sequential decision problems. However, learning policies that can generalize predictably acr…
cs.LG2022
Reusable Options through Gradient-based Meta Learning
David Kuric, Herke van Hoof
Hierarchical methods in reinforcement learning have the potential to reduce the amount of decisions that the agent needs to perform when learning new tasks. However, finding reusab…