We present Pure-Past Action Masking (PPAM), a lightweight approach to action masking for
safe reinforcement learning. In PPAM, actions are disallowed (“masked”) according to …

This paper introduces a novel physics-informed exploration strategy for a deep
reinforcement learning (DRL)-based energy management system (EMS), specifically …

Featured Application Synchromodal optimisation; decision support systems; dynamical
transport optimisation. Abstract Intermodal freight transport (IFT) requires a large number of …

The curse of rarity—the rarity of safety-critical events in high-dimensional variable spaces—
presents significant challenges in ensuring the safety of autonomous vehicles using deep …

For safe operation, autonomous vehicles have to obey traffic rules that are set forth in legal
documents formulated in natural language. Temporal logic is a suitable concept to formalize …

This study presents a novel methodology incorporating safety constraints into a robotic
simulation during the training of deep reinforcement learning (DRL). The framework …

Recently, the autonomous driving technology is at a critical phase evolving from typical,
closed scenarios to largescale, open driving scenarios, which is challenged by the diversity …

Reinforcement learning (RL) controllers are flexible and performant but rarely guarantee
safety. Safety filters impart hard safety guarantees to RL controllers while maintaining …

The growing complexity of power system management has led to an increased interest in
the use of reinforcement learning (RL). However, no tool for comprehensive and realistic …

Among approaches for provably safe reinforcement learning, Model Predictive Shielding
(MPS) has proven effective at complex tasks in continuous, high-dimensional state spaces …