Nowadays, society faces several traffic related problems, such as traffic jams, time loss, lower traffic safety, increased pollution, etc., especially in urban areas. This is caused by high traffic volumes, which often exceed the capacity of the road infrastructure, particularly in peak hours. A common way of managing traffic in urban areas is traffic light control, which plays a key role in traffic safety and efficiency. To reduce delays the traffic light controllers should adjust to changing traffic volumes continuously and rapidly. Limited possibilities for road infrastructure extensions and growing traffic volumes represent a challenge for existent control techniques with increasing problem of maintaining suitable level of service. When unexpected events occur, the disadvantage of current traffic control system is even more evident. Stochastic nature of traffic and constant changes in traffic flow requires continuous adaption of traffic light controller. For solving complex problem of traffic lights optimization the system that continuously adapts and learns should be employed. Artificial intelligence approaches enable development of self-learning systems. The thesis presents a novel approach for solving problems of traffic light controller optimization with use of the reinforcement learning. The proposed algorithm enables fast and self-learning optimal strategy of traffic control in different traffic conditions. The efficiency of proposed algorithm was tested using a micro simulation tool, which simulates traffic conditions with great accuracy. The results of the performed experiments show that proposed algorithm outperforms the actuated signal controllers.