マルチエージェント環境における分散型干渉除去
Distributed interference cancellation in multi-agent scenarios
ノイズや故障を含むエージェント間の通信で、各エージェントの振る舞いに応じて共有重みを適応的に調整し干渉を抑える分散アルゴリズムを提案し、マルチエージェント強化学習と拡散LMSで有効性を示した論文。
著者: Mahdi Shamsi, Alireza Moslemi Haghighi, Farokh Marvasti
分類: cs.MA, cs.LG, cs.RO, cs.SY, eess.SP, eess.SY
原文アブストラクト
This paper considers the problem of detecting impaired and noisy nodes over network. In a distributed algorithm, lots of processing units are incorporating and communicating with each other to reach a global goal. Due to each one's state in the shared environment, they can help the other nodes or mislead them (due to noise or a deliberate attempt). Previous works mainly focused on proper locating agents and weight assignment based on initial environment state to minimize malfunctioning of noisy nodes. We propose an algorithm to be able to adapt sharing weights according to behavior of the agents. Applying the introduced algorithm to a multi-agent RL scenario and the well-known diffusion LMS demonstrates its capability and generality.