Correct-by-construction synthesis is a cornerstone of the confluence of formal methods and
control theory towards designing safety-critical systems. Instead of following the time-tested …

Reinforcement learning (RL) methods usually treat reward functions as black boxes. As
such, these methods must extensively interact with the environment in order to discover …

Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …

We study the problem of learning control policies for complex tasks given by logical
specifications. Recent approaches automatically generate a reward function from a given …

Abstract Reward Machines (RMs), originally proposed for specifying problems in
Reinforcement Learning (RL), provide a structured, automata-based representation of a …

This letter investigates the motion planning of autonomous dynamical systems modeled by
Markov decision processes (MDP) with unknown transition probabilities over continuous …

Reinforcement learning (RL) agents seek to maximize the cumulative reward obtained when
interacting with their environment. Users define tasks or goals for RL agents by designing …

We address the problem of teaching a deep reinforcement learning (RL) agent to follow
instructions in multi-task environments. Instructions are expressed in a well-known formal …

The translation from temporal logics to automata is the workhorse algorithm of several
techniques in computer science and AI, such as reactive synthesis, reasoning about actions …

Reinforcement Learning (RL) has achieved tremendous development in recent years, but
still faces significant obstacles in addressing complex real-life problems due to the issues of …