Distributed interference cancellation in multi-agent scenarios
This addresses the issue of noise and malfunctions in distributed systems for applications like robotics or sensor networks, but it is incremental as it builds on prior work on weight assignment.
The paper tackles the problem of detecting impaired and noisy nodes in multi-agent networks by proposing an adaptive algorithm that adjusts sharing weights based on agent behavior, demonstrating its effectiveness in multi-agent RL and diffusion LMS scenarios.
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.