4 papers · 1 filter
Probabilistic Performance Guarantees for Multi-Task Reinforcement Learning
Yannik Schnitzer, Mathias Jackermeier, Alessandro Abate +1
Multi-task reinforcement learning trains generalist policies that can execute multiple tasks. While recent years have seen significant progress, existing approaches rarely provide…
PlatoLTL: Learning to Generalize Across Symbols in LTL Instructions for Multi-Task RL
Jacques Cloete, Mathias Jackermeier, Ioannis Havoutis +1
A central challenge in multi-task reinforcement learning (RL) is to train generalist policies capable of performing tasks not seen during training. To facilitate such generalizatio…
Robust Parameter Learning for Uncertain MDPs
Yannik Schnitzer, Alessandro Abate, David Parker
Learning-based approaches to verifying unknown Markov decision processes (MDPs) often employ uncertain MDPs. These models use, for example, confidence intervals to capture transiti…
Zero-Shot Instruction Following in RL via Structured LTL Representations
Mathias Jackermeier, Mattia Giuri, Jacques Cloete +1
We study instruction following in multi-task reinforcement learning, where an agent must zero-shot execute novel tasks not seen during training. In this setting, linear temporal lo…