This thesis addresses the development of a resilient task sequence planning framework for industrial mobile robots using reinforcement learning. The thesis will focus on how best to enhance the adaptiveness and efficiency of robots in dynamic and, subsequently, unpredictable industrial environments. In the proposed framework, Markov Decision Processes are to be used for modeling the environment in which the robot makes a decision. In contrast, several RL algorithms like Q-learning, SARSA, and Dyna-Q are proposed to optimize navigation and task execution. The key contributions of this thesis are the proposition of new models for efficient robot navigation, implementation of robust RL algorithms in Python, and a simulation framework that could validate the resilience of the robot against worst-case scenarios.