Results 241 to 250 of about 9,252,589 (289)

Formal Verification for Safe Deep Reinforcement Learning in Trajectory Generation

2020 Fourth IEEE International Conference on Robotic Computing (IRC), 2020
We consider the problem of Safe Deep Reinforcement Learning (DRL) using formal verification in a trajectory generation task. In more detail, we propose an approach to verify whether a trained model can generate trajectories that are guaranteed to meet safety properties (e.g., operate in a limited work-space).
Davide Corsi   +3 more
openaire   +1 more source

Safe deep reinforcement learning for building energy management

Applied Energy
Peng Wang, Renke Huang, Javier Arroyo
exaly   +2 more sources

Verifying Online Safety Properties for Safe Deep Reinforcement Learning

ACM Transactions on Intelligent Systems and Technology
Ensuring safety in reinforcement learning (RL) is critical for deploying agents in real-world applications. During training, current safe RL approaches often rely on indicator cost functions that provide sparse feedback, resulting in two key limitations: (i) poor sample efficiency due to the lack of safety information in ...
Alessandro Farinelli   +2 more
exaly   +4 more sources

Safe Deep Reinforcement Learning for Adaptive Cruise Control by Imposing State-Specific Safe Sets

2021 IEEE Intelligent Vehicles Symposium (IV), 2021
Deep reinforcement learning has been increasingly discussed for solving continuous control tasks in the field of autonomous driving and driver assistance systems. However, trial-and-error learning and the black-box character of neural networks make it prone to accidental damage in safety-critical environments.
Brosowsky, Mathis   +5 more
openaire   +2 more sources

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