WRFMR: A Multi-Agent Reinforcement Learning Method for Cooperative Tasks
Multi-agent reinforcement learning (MARL) for cooperative tasks has been extensively studied in recent years. The balance of exploration and exploitation is crucial to MARL algorithms' performance in terms of the learning speed and the quality of the obtained strategy. To this end, we propose a...
| 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/9272805/ |