In the era of Industry 4.0, optimizing production processes has become increasingly critical due to the high demand for efficiency, flexibility, and customization in manufacturing. The Job Shop Scheduling Problem (JSSP), a prominent NP-hard problem, plays a pivotal role in this context, requiring the scheduling of jobs with multiple operations on specific machines in a predetermined order. Effective solutions to JSSP are essential for minimizing production time, reducing costs, and enhancing overall productivity. This thesis presents the development and evaluation of a single-agent reinforcement learning algorithm designed to address both the JSSP and its dynamic variant (DJSSP). The primary objective of this research is to test the efficiency and adaptability of reinforcement learning algorithm for scheduling solutions in both deterministic and dynamic environments characterized by variability and uncertainty.