We present a verification-based learning framework VEL that synthesizes safe programmatic
controllers for environments with continuous state and action spaces. The key idea is the …

Despite the tremendous advances that have been made in the last decade on developing
useful machine-learning applications, their wider adoption has been hindered by the lack of …

Reinforcement learning is an approach to controller synthesis where agents rely on reward
signals to choose actions in order to satisfy the requirements implicit in reward signals …

Formal verification provides a high degree of confidence in safe system operation, but only if
reality matches the verified model. Although a good model will be accurate most of the time …

State-of-the-art machine-learned controllers for autonomous systems demonstrate
unbeatable performance in scenarios known from training. However, in evolving …

The desire to use reinforcement learning in safety-critical settings has inspired a recent
interest in formal methods for learning algorithms. Existing formal methods for learning and …

Parameterized synthesis offers a solution to the problem of constructing correct and verified
controllers for parameterized systems. Such systems occur naturally in practice (eg, in the …

A controller is a device that interacts with a plant. At each time point, it reads the plant's state
and issues commands with the goal that the plant operates optimally. Constructing optimal …

In the current control design of safety-critical autonomous systems, formal verification
techniques are typically applied after the controller is designed to evaluate whether the …

We study the problem of generating interpretable and verifiable policies for Reinforcement
Learning (RL). Unlike the popular Deep Reinforcement Learning (DRL) paradigm, in which …