Target-Network Update Linked with Learning Rate Decay Based on Mutual Information and Reward in Deep Reinforcement Learning
In this study, a target-network update of deep reinforcement learning (DRL) based on mutual information (MI) and rewards is proposed. In DRL, updating the target network from the Q network was used to reduce training diversity and contribute to the stability of learning. If it is not properly update...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/15/10/1840 |