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In this study, we propose a novel model to design dynamic hotel room pricing strategies that consider the specific requirements associated with the tourism sector. Reinforcement learning (RL) is used to formulate the problem as a Markov decision process (MDP) and Q-learning is used to solve this problem with a new reward function for hotel room pricing which considers both the profit and demand. In the proposed model, the basic features of the hotels are digitized and expressed in a way that similar hotels get close values. In this way, price predictions for the hotels that are newly included in the system can be made through similar hotels and the cold start problem is solved. In order to observe the performance of the proposed model, we used a real-world dataset provided by a tourism agency in Turkey and the results show that the proposed model achieves less mean absolute percentage error on test data. In …