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cs.LG2026
Deep Epistemic Value Functions for Optimistic Exploration
Leander Diaz-Bone, Marco Bagatella, Jonas Hübotter +1
Principled exploration in reinforcement learning requires an agent to quantify its epistemic uncertainty and act to resolve it. Uncertainty over the value function provides a natur…
cs.LG2025
Learning on the Job: Test-Time Curricula for Targeted Reinforcement Learning
Jonas Hübotter, Leander Diaz-Bone, Ido Hakimi +2
Humans are good at learning on the job: We learn how to solve the tasks we face as we go along. Can a model do the same? We propose an agent that assembles a task-specific curricul…
cs.LG2025
DISCOVER: Automated Curricula for Sparse-Reward Reinforcement Learning
Leander Diaz-Bone, Marco Bagatella, Jonas Hübotter +1
Sparse-reward reinforcement learning (RL) can model a wide range of highly complex tasks. Solving sparse-reward tasks is RL's core premise, requiring efficient exploration coupled…