SYSTEMS AND METHODS FOR A SELF-LEARNING, RESILIENT REINFORCEMENT-LEARNING AGENT

Abstract

Systems and methods for enterprise production scheduling using a self-learning, resilient Reinforcement Learning (RL) agent. The RL agent interacts with a simulated production environment modeled as a dynamic graph, enabling efficient handling of complex multi-stage scheduling dependencies. Through iterative training, inference, and continuous learning modes, the agent autonomously learns optimal scheduling policies, adapts to evolving production conditions, and incorporates user preferences. The system includes components such as a data profiler for historical analysis, a synthesizer for training data generation, and an initializer for environment setup. The RL agent generates multiple feasible schedules, refines its policy based on feedback, and significantly reduces computational overhead compared to traditional heuristics and genetic algorithms.

CPC Classifications