4 papers · 1 filter
SemML 2.0: Synthesizing Controllers for LTL
Jan KÅetÃnský, Tobias Meggendorfer, Maximilian Prokop
Synthesizing a reactive system from specifications given in linear temporal logic (LTL) is a classical problem, finding its applications in safety-critical systems design. These sy…
Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions
Alessandro Abate, Giuseppe De Giacomo, Mathias Jackermeier +4
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…
SemML: Enhancing Automata-Theoretic LTL Synthesis with Machine Learning
Jan Kretinsky, Tobias Meggendorfer, Maximilian Prokop +1
Synthesizing a reactive system from specifications given in linear temporal logic (LTL) is a classical problem, finding its applications in safety-critical systems design. We prese…
MULTIGAIN 2.0: MDP controller synthesis for multiple mean-payoff, LTL and steady-state constraints
Severin Bals, Alexandros Evangelidis, Jan KÅetÃnský +1
We present MULTIGAIN 2.0, a major extension to the controller synthesis tool MULTIGAIN, built on top of the probabilistic model checker PRISM. This new version extends MULTIGAIN's…