A Deep Reinforcement Learning Model for Humanoid Robot Navigation and Task Scheduling in Automated Supply Chains

Humanoid robots must navigate, decide, and schedule efficiently to boost automated supply chain efficiency. Traditional rule-based techniques fail in dynamic situations, especially with task dependencies. A unique Puma Optimizer-mutated Twin-Stage Adaptive Twin-Delayed Deep Deterministic Policy Gradient (PO-TSATD3) method is used in this deep reinforcement learning system. Training datasets imitate real-world logistics situations with dynamic impediments, many robots, and varying workloads. Data preparation cleans and normalizes for quality. The Puma Optimizer optimises convergence and operating efficiency, while the PO-TSATD3 framework improves navigation and scheduling adaptive learning. Python simulations show considerable gains in navigation accuracy, collision reduction, and schedule optimization over conventional methods. The model's outstanding performance metrics proved its scalability and durability in complicated situations. This research validates the application of deep reinforcement learning, augmented by PO-TSATD3, as a powerful solution for intelligent humanoid robot operations in future supply chain systems.