Deep reinforcement learning (DRL) has proven capable of superhuman performance on
many complex tasks. To achieve this success, DRL algorithms train a decision-making agent …

This paper concerns the efficient construction of a safety shield for reinforcement learning.
We specifically target scenarios that incorporate uncertainty and use Markov decision …

Formally verifying that reinforcement learning systems act safely is increasingly important,
but existing methods only verify over finite time. This is of limited use for dynamical systems …

This paper presents COOL-MC, a tool that integrates state-of-the-art reinforcement learning
(RL) and model checking. Specifically, the tool builds upon the OpenAI gym and the …

Path planning and task scheduling are two challenging problems in the design of multiple
autonomous agents. Both problems can be solved by the use of exhaustive search …

Moving block railway systems are the next generation signalling systems currently under
development as part of the Shift2Rail European initiative, including autonomous driving …

Neural networks (NN) are taking over ever more decisions thus far taken by humans, even
though verifiable system-level guarantees are far out of reach. Neither is the verification …

Safe and optimal controller synthesis for switched-controlled hybrid systems, which combine
differential equations and discrete changes of the system's state, is known to be intricately …

Deep reinforcement learning is an increasingly popular technique for synthesising policies
to control an agent's interaction with its environment. There is also growing interest in …

Planning is a critical function of multi-agent autonomous systems, which includes path
finding and task scheduling. Exhaustive search-based methods such as model checking …