7 papers
Jaxolotl: A Unified High-Performance Benchmark Suite for LTL-Based Multi-Task RL
Mathias Jackermeier, Jacques Cloete, Alessandro Abate
Training agents to follow arbitrary instructions is an important goal of multi-task reinforcement learning (RL). Linear temporal logic (LTL) provides a precise and structured forma…
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…
Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions
Alessandro Abate, Giuseppe De Giacomo, Mathias Jackermeier +3
We study multi-task reinforcement learning (RL), a setting in which an agent learns a single, universal policy capable of generalising to arbitrary, possibly unseen tasks. We consi…
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: Scaling LTL-Guided Multi-Task RL
Jacques Cloete, Mathias Jackermeier, Ioannis Havoutis +1
Linear temporal logic (LTL) has emerged as a powerful formalism for specifying structured, temporally extended tasks in multi-task reinforcement learning (RL). However, while exist…
Zero-Shot Instruction Following in RL via Structured LTL Representations
Mattia Giuri, Mathias Jackermeier, Alessandro Abate
Linear temporal logic (LTL) is a compelling framework for specifying complex, structured tasks for reinforcement learning (RL) agents. Recent work has shown that interpreting LTL i…
DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL
Mathias Jackermeier, Alessandro Abate
Linear temporal logic (LTL) has recently been adopted as a powerful formalism for specifying complex, temporally extended tasks in multi-task reinforcement learning (RL). However,…