Deep Reinforcement Learning by Balancing Offline Monte Carlo and Online Temporal Difference Use Based on Environment Experiences
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 complica...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/12/10/1685 |