17 papers
In reinforcement learning (RL), agents learn to act by trial and error, gradually improving their performance at the For many applications of reinforcement learn- task as learning progresses. Recent work in deep RL as- ing it can be more convenient to specify both sumes that agen...
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...
However, there remain significant barriers to their deploy- ment in safety-critical engineering applications. Autonomous We propose a framework for verifiable and compositional vehicles, manufacturing robotics, and power systems manage- reinforcement learning (RL) in which a coll...
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...
by (default or sampled) safe ones; action projection, e.g., (Cheng et al. 2019), where unsafe actions are replaced by We present Pure-Past Action Masking (PPAM), a lightweight approach to action masking for safe reinforcement learning. projecting them to the safe action space; an...
Many applications, e.g., in shared mobility, require coordinating a large number of agents. Mean-field reinforcement learning ad- dresses the resulting scalability challenge by optimizing the policy of a representative agent interacting with the infinite population of identical a...
approaches aim to provide high-assurance quantitative guar- Ensuring that AI systems reliably and robustly antees about the safety of an AI system’s behaviour through avoid harmful or dangerous behaviours is a cru- the use of three core components — a formal safety specifi- cial...
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...