Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Daukantas, Ieva"'
Autor:
Mandal, Udayan, Amir, Guy, Wu, Haoze, Daukantas, Ieva, Newell, Fletcher Lee, Ravaioli, Umberto, Meng, Baoluo, Durling, Michael, Hobbs, Kerianne, Ganai, Milan, Shim, Tobey, Katz, Guy, Barrett, Clark
In recent years, deep reinforcement learning (DRL) approaches have generated highly successful controllers for a myriad of complex domains. However, the opaque nature of these models limits their applicability in aerospace systems and safety-critical
Externí odkaz:
http://arxiv.org/abs/2407.07088
Autor:
Mandal, Udayan, Amir, Guy, Wu, Haoze, Daukantas, Ieva, Newell, Fletcher Lee, Ravaioli, Umberto J., Meng, Baoluo, Durling, Michael, Ganai, Milan, Shim, Tobey, Katz, Guy, Barrett, Clark
Deep reinforcement learning (DRL) is a powerful machine learning paradigm for generating agents that control autonomous systems. However, the ``black box'' nature of DRL agents limits their deployment in real-world safety-critical applications. A pro
Externí odkaz:
http://arxiv.org/abs/2405.14058
Quantization replaces floating point arithmetic with integer arithmetic in deep neural network models, providing more efficient on-device inference with less power and memory. In this work, we propose a framework for formally verifying properties of
Externí odkaz:
http://arxiv.org/abs/2312.12679