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pro vyhledávání: '"Newell, Fletcher Lee"'
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