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 …

The need for computationally-efficient control methods of dynamical systems under temporal
logic tasks has recently become more apparent. Existing methods are computationally …

In control theory, complex models of physical processes, such as systems of differential or
difference equations, are usually checked against simple specifications, such as stability …

In recent years, formal methods have been extensively used in the design of autonomous
systems. By employing mathematically rigorous techniques, formal methods can provide …

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 …

Reinforcement learning (RL) depends critically on the choice of reward functions used to
capture the desired behavior and constraints of a robot. Usually, these are handcrafted by a …

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 …

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

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 …