Título
Fear Field: Adaptive constraints for safe environment transitions in Shielded Reinforcement LearningOtras instituciones
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© 2023 The AuthorsAcceso
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Proceedings of the IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL), co-located with the 32nd International Joint Conference on Artificial Intelligence (IJCAI2023) Macao, June, 2023Editor
CEUR-WS.orgPalabras clave
Reinforcement Learning
Shielding
Adaptive constraints
Robustness ... [+]
Shielding
Adaptive constraints
Robustness ... [+]
Reinforcement Learning
Shielding
Adaptive constraints
Robustness
Safe AI
ODS 9 Industria, innovación e infraestructura [-]
Shielding
Adaptive constraints
Robustness
Safe AI
ODS 9 Industria, innovación e infraestructura [-]
Resumen
Shielding methods for Reinforcement Learning agents show potential for safety-critical industrial applications. However, they still lack robustness on nominal safety, a key property for safety control ... [+]
Shielding methods for Reinforcement Learning agents show potential for safety-critical industrial applications. However, they still lack robustness on nominal safety, a key property for safety control systems. In the case of a significant change in the environment dynamic, shielding methods cannot guarantee safety until their inherent dynamics model is updated to the new scenario. The agent could reach risky states because the model cannot predict well. These situations could lead to catastrophic outcomes, such as damage to the cyber-physical system or loss of human lives, which are not allowed on safety-critical applications. The novel method presented in this paper, Fear Field, replicates human behaviour in those scenarios, adapting safety constraints whenever a drastic environmental change is introduced. Fear Field reduces safety violations by one order of magnitude compared to an RL agent implementing only a shield. [-]
Colecciones
- Congresos - Ingeniería [341]
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