How can LLMs manage smart factory robots?
Multi-Agent Deep Q-Network with Layer-based Communication Channel for Autonomous Internal Logistics Vehicle Scheduling in Smart Manufacturing
This paper proposes a multi-agent deep reinforcement learning system (MADQN with LBCC) to schedule autonomous vehicles (AIVs) within a smart factory, optimizing for minimal job tardiness and low energy consumption.
Relevant to LLM-based multi-agent systems are the decentralized nature of the agents (each job is an agent), the layer-based communication channel (LBCC) used to address non-stationarity and improve coordination between agents, the focus on real-time dynamic scheduling adapting to unexpected events, and the use of deep Q-networks for decision-making (workstation selection and AIV assignment). This demonstrates the potential of combining multi-agent RL with deep learning for complex resource allocation in dynamic environments, akin to those found in multi-agent LLM applications.