We present a reinforcement learning (RL) frame-work to synthesize a control policy from a
given linear temporal logic (LTL) specification in an unknown stochastic environment that …

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 …

Limit-deterministic Büchi automata can replace deterministic Rabin automata in probabilistic
model checking algorithms, and can be significantly smaller. We present a direct …

The formal analysis of critical systems is supported by a vast space of modelling formalisms
and tools. The variety of incompatible formats and tools however poses a significant …

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 …

This paper studies optimal motion planning subject to motion and environment uncertainties.
By modeling the system as a probabilistic labeled Markov decision process (PL-MDP), the …

The expanding role of reinforcement learning (RL) in safety-critical system design has
promoted ω-automata as a way to express learning requirements—often non-Markovian …

We study the problem of policy optimization (PO) with linear temporal logic (LTL) constraints.
The language of LTL allows flexible description of tasks that may be unnatural to encode as …

Mungojerrie is an extensible tool that provides a framework to translate linear-time
objectives into reward for reinforcement learning (RL). The tool provides convergent RL …

Recent success in reinforcement learning (RL) has brought renewed attention to the design
of reward functions by which agent behavior is reinforced or deterred. Manually designing …