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Multi-agent learning via gradient ascent activity-based credit assignment
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DOI：10.1038/s41598-023-42448-9.png)
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摘要
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We consider the situation in which cooperating agents learn to achieve a common goal based solely on a global return that results from all agents' behavior. The method proposed is based on taking into account the agents' activity, which can be any additional information to help solving multi-agent decentralized learning problems. We propose a gradient ascent algorithm and assess its performance on synthetic data.
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3.9
论文数:
28.0W
被引数:
83.5W
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