Adaptive Exploration Strategy With Multi-Attribute Decision-Making for Reinforcement Learning
Reinforcement Learning (RL) agents often encounter the bottleneck of the performance when the dilemma of exploration and exploitation arises. In this study, an adaptive exploration strategy with multi-attribute decision-making is proposed to address the trade-off problem between exploration and expl...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8993720/ |