This thesis explores the application of the Q-learning algorithm for learning robotic manipulation tasks in simulated environments, specifically Mountain-Car and Cart-Pole. Q-learning is an off-policy reinforcement learning algorithm that learns optimal actions based on interaction with the environment and received rewards. Experimental results demonstrate that the Q-learning algorithm successfully taught agents to perform complex manipulation tasks, achieving high performance in both environments. In the Mountain-Car environment, the agent learned to use the car's inertia to climb the hill, while in the Cart-Pole environment, the agent successfully learned to balance the pole on the cart. The advantages of the Q-learning algorithm include simplicity, robustness, and guaranteed convergence to the optimal policy with proper hyperparameter tuning. However, the algorithm has limitations related to the discretization of state spaces and memory requirements. Future work suggests applying deep Q-learning, advanced exploration strategies, hyperparameter optimization, application to real robotic systems, and exploring hybrid approaches to improve the efficiency and scalability of the algorithm. These results provide a basis for further research and application of the Q-learning algorithm in real robotic systems.