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- W4385944431 abstract "Many multi-agent systems require inter-agent communication to properly achieve their goal. By learning the communication protocol alongside the action protocol using multi-agent reinforcement learning techniques, the agents gain the flexibility to determine which information should be shared. However, when the number of agents increases we need to create an encoding of the information contained in these messages. In this paper, we investigate the effect of increasing the amount of information that should be contained in a message and increasing the number of agents. We evaluate these effects on two different message encoding methods, the mean message encoder and the attention message encoder. We perform our experiments on a matrix environment. Surprisingly, our results show that the mean message encoder consistently outperforms the attention message encoder. Therefore, we analyse the communication protocol used by the agents that use the mean message encoder and can conclude that the agents use a combination of an exponential and a logarithmic function in their communication policy to avoid the loss of important information after applying the mean message encoder." @default.
- W4385944431 created "2023-08-18" @default.
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- W4385944431 date "2023-08-09" @default.
- W4385944431 modified "2023-09-23" @default.
- W4385944431 title "Scalability of Message Encoding Techniques for Continuous Communication Learned with Multi-Agent Reinforcement Learning" @default.
- W4385944431 doi "https://doi.org/10.48550/arxiv.2308.04844" @default.
- W4385944431 hasPublicationYear "2023" @default.
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