A Model-Free Deep Reinforcement Learning Algorithm for Solving Multi-Agent Nash Equilibrium With Unstable Communication
Most reinforcement learning (RL) algorithms proposed to solve Nash equilibrium in multi-agent systems assume stable communication conditions or rely on accurate models of the environment. However, these assumptions are often unrealistic in practical applications since communication is not always sta...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10916637/ |
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