چكيده به لاتين
In this dissertation, we propose a new incremental model for acquiring skills and using them in intrinsically motivated reinforcement learning. In this model, we let the agent use different intrinsic motivation factors for acquiring skills and exploring the environment. Also, we present the new idea of evaluating and pruning independent skills, which has not been taken into account in the related work. In the proposed model, the learning process is divided into two phases. In the first phase which is called the developmental period, the agent explores the environment and acquires task-independent skills by using intrinsic motivation mechanisms. In the second phase which is called solving the external task period, the previously learned skills are granted to the agent and it evaluates them to find the suitable ones for learning a specific task. Task-independent skills can be used for accelerating other similar tasks. In this dissertation, the being of cause, novelty and imitation motivations are used to provide methods for skill acquisition and the curiosity motivation to explore the environment. We propose a computational model for each of these motivations. In addition, we propose four new skill evaluation methods in the second phase. Experimental results in four domains show that the proposed methods significantly increase the learning speed. The results of using the skills acquired by the methods presented in this thesis also show a significant advantage over the other methods presented in the field of skill acquisition.
