9 papers
. Scalability issues may prevent users from verifying critical proper- ties of a complex hardware design. In this situation, we propose to synthesize a “safety shield” that is attached to the design to enforce the properties at run time. Shield synthesis can succeed where model c...
reward Environment Learning Agent Reinforcement learning algorithms discover policies that observation maximize reward, but do not necessarily guarantee safety dur- actions ing learning or execution phases. We introduce a new ap- proach to learn optimal policies while enforcing p...
A recent work [1] developed a shielding framework for single- Multi-agent reinforcement learning (MARL) has been increasingly agent reinforcement learning (RL), which synthesizes a shield to used in a wide range of safety-critical applications, which require enforce the correctne...
A common cardiac arrhythmia, known as heart block or AV block, is a condition where the electrical signal con- As the complexity of artificial pacemakers continues to grow, trolling the ventricular contractions are partially or com- the importance of capturing its functional corr...
In this paper, we present the first MA shielding approach that is Deep reinforcement learning has emerged as a powerful tool for truly compositional, does not require online communication, and obtaining high-performance policies. However, the safety of these provides absolute saf...
Trust in a decision-making system requires both safety guarantees and the ability to interpret and understand its behavior. This is particularly important for learned systems, whose decision-making processes are often highly opaque. Shielding is a prominent model-based technique...
at least some probability p. This framework comes from a probabilistic version of what is usually called the “safety” In real-life scenarios, a Reinforcement Learning (RL) agent aiming to maximise their reward, must often also behave in a fragment of Linear Temporal Logic (LTL) (...
not assume access to a simulator or environment abstraction, and Safety is a major concern in reinforcement learning (RL): we aim at is compatible with continuous environments. developing RL systems that not only perform optimally, but are also We also show that optimizing over s...