Process-Informed Multi-Agent Reinforcement Learning for Joint Radio-Compute Orchestration in Industrial O-RAN

Industrial open radio access network (O-RAN) systems must support manufacturing workloads with dynamically varying communication and computation demands driven by production processes. Existing O-RAN orchestration mechanisms remain largely network-centric and fail to capture such process-induced variability. This letter proposes a decentralized process-informed reinforcement learning (RL) framework for joint radio and compute orchestration in industrial O-RAN. Distributed agents at radio units (RUs) adapt physical resource block (PRB) and compute allocations based on workload and latency requirements. Using a multi-agent dueling deep Q-network architecture, the approach improves latency fulfillment and reliability under nonstationary industrial traffic. Simulation results demonstrate consistent gains over network-centric multi-agent baselines.