用LSTM编码压缩机器人通信，省流量还更准。
Communication Reduction via Semantic-Based Encoding in DMPC Using LSTMs
- 用LSTM构建编码解码器，压缩分布式控制信息
- 压缩后仍保持良好控制性能，支持不同预测时长
- 适合资源受限的多智能体协同系统
分布式模型预测控制（DMPC）的通信需求可能超过先进无线技术的承载能力，因为各智能体每时间步至少需交换大量信息。为语义降低通信负担，本文在分布式优化算法中引入基于长短期记忆（LSTM）单元的编码-解码网络。智能体发布消息的压缩表示，接收方在收到后重建原始信息。在移动机器人编队测试中，训练后的网络在大幅减少通信量的情况下仍保持满意性能，并在全通信无法承受的条件下可靠运行。结果表明，使用LSTM可实现前所未有的重建精度，或在不重新训练的前提下支持不同预测时长。
原文摘要 · Abstract (English)
The communication demands of distributed model prediction control (DMPC) can overwhelm even advanced wireless communication technologies as agents must exchange a significant amount of information at least once per time step. To semantically reduce communication demands, this work employs encoder-decoder networks built around long-short term memory (LSTM) cells in a distributed optimization algorithm. Agents publish a reduced representation of a message and receivers reconstruct the original message upon reception. In tests with reduced communication using formations of mobile robots, trained networks retain satisfactory performance and work reliably under conditions overwhelming full communication. As the results show, the usage of LSTMs either allows unprecedented reconstruction accuracy or the usage of different prediction-horizon lengths without the necessity to retrain.
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