We present a model-free reinforcement learning algorithm to synthesize control policies that
maximize the probability of satisfying high-level control objectives given as Linear Temporal …

We provide the first solution for model-free reinforcement learning of ω-regular objectives for
Markov decision processes (MDPs). We present a constructive reduction from the almost …

We present the first model-free Reinforcement Learning (RL) algorithm to synthesise
policies for an unknown Markov Decision Process (MDP), such that a linear time property is …

The combination of data-driven learning methods with formal reasoning has seen a surge of
interest, as either area has the potential to bolstering the other. For instance, formal methods …

We present the library Owl (O mega-W ords, automata, and L TL) for ω-automata and linear
temporal logic. It forms a backbone of several translations from LTL to automata and related …

Facts proved that obeying traffic laws keeps the promise to promote the safety of self-driving
vehicles. Current self-driving vehicles usually have fixed algorithms during autonomous …

Reinforcement Learning (RL) is a widely employed machine learning architecture that has
been applied to a variety of control problems. However, applications in safety-critical …

The difficulty of manually specifying reward functions has led to an interest in using linear
temporal logic (LTL) to express objectives for reinforcement learning (RL). However, LTL …

We propose an actor-critic, model-free, and online Reinforcement Learning (RL) framework
for continuous-state continuous-action Markov Decision Processes (MDPs) when the reward …

LCRL is a software tool that implements model-free Reinforcement Learning (RL) algorithms
over unknown Markov Decision Processes (MDPs), synthesising policies that satisfy a given …