A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
Owing to the complexity involved in training an agent in a real-time environment, e.g., using the Internet of Things (IoT), reinforcement learning (RL) using a deep neural network, i.e., deep reinforcement learning (DRL) has been widely adopted on an online basis without prior knowledge and complicated reward functions. DRL can handle a symmetrical balance between bias and variance—this indicates that the RL agents are competently trained in real-world applications. The approach of the proposeddoi:10.3390/sym12101685 fatcat:kzwiywbhfzgo3lbuo4qjvcayfu