Safe reinforcement learning for power system control: A review
The large-scale integration of intermittent renewable energy resources introduces increased
uncertainty and volatility to the supply side of power systems, thereby complicating system …
uncertainty and volatility to the supply side of power systems, thereby complicating system …
Intelligent computational techniques for physical object properties discovery, detection, and prediction: A comprehensive survey
The exploding usage of physical object properties has greatly facilitated real-time
applications such as robotics to perceive exactly as it appears in existence. Changes in the …
applications such as robotics to perceive exactly as it appears in existence. Changes in the …
Provably safe reinforcement learning via action projection using reachability analysis and polynomial zonotopes
While reinforcement learning produces very promising results for many applications, its main
disadvantage is the lack of safety guarantees, which prevents its use in safety-critical …
disadvantage is the lack of safety guarantees, which prevents its use in safety-critical …
Safe Reinforcement Learning for Automated Vehicles via Online Reachability Analysis
X Wang, M Althoff - IEEE Transactions on Intelligent Vehicles, 2023 - ieeexplore.ieee.org
Ensuring safe and capable motion planning is paramount for automated vehicles.
Traditional methods are limited in their ability to handle complex and unpredictable traffic …
Traditional methods are limited in their ability to handle complex and unpredictable traffic …
A human-centered safe robot reinforcement learning framework with interactive behaviors
Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real
world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL) is …
world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL) is …
Safe reinforcement learning for urban driving using invariably safe braking sets
Deep reinforcement learning (RL) has been widely applied to motion planning problems of
autonomous vehicles in urban traffic. However, traditional deep RL algorithms cannot …
autonomous vehicles in urban traffic. However, traditional deep RL algorithms cannot …
Pure-past action masking
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 …
safe reinforcement learning. In PPAM, actions are disallowed (“masked”) according to …
Safe Reinforcement Learning for Energy Management of Electrified Vehicle with Novel Physics-Informed Exploration Strategy
A Biswas, M Acquarone, H Wang… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
This paper introduces a novel physics-informed exploration strategy for a deep
reinforcement learning (DRL)-based energy management system (EMS), specifically …
reinforcement learning (DRL)-based energy management system (EMS), specifically …
Curse of rarity for autonomous vehicles
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 …
presents significant challenges in ensuring the safety of autonomous vehicles using deep …
Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea
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 …
documents formulated in natural language. Temporal logic is a suitable concept to formalize …