This thesis investigates Reinforcement Learning (RL) for the Dynamic Job Shop Scheduling Problem (DJSSP), where agents make sequencing decisions under random job arrivals and tardiness is realized only upon job completion. This work argues that asynchronous per-machine decisions mitigate the credit-assignment challenge and assist training stability, motivating designs that explicitly align rewards with the causality of shop-floor events. The scheduler adopts a Centralized-Training and Decentralized-Execution (CTDE) scheme with parameter sharing and an event-driven policy that acts only at irregular decision epochs. This preserves local detail while remaining size-agnostic as queues fluctuate. State is constructed leveraging a “Minimal Repetition” encoder that packs the top job candidates of each machine into fixed slots with job-specific features, enabling direct job selection without fixing problem size.