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Full-Text Articles in Computer Engineering
Intrinsically Motivated Exploration In Hierarchical Reinforcement Learning, Christopher M. Vigorito
Intrinsically Motivated Exploration In Hierarchical Reinforcement Learning, Christopher M. Vigorito
Doctoral Dissertations
The acquisition of hierarchies of reusable skills is one of the distinguishing characteristics of human intelligence, and the learning of such hierarchies is an important open problem in computational reinforcement learning (RL). In humans, these skills are learned during a substantial developmental period in which individuals are intrinsically motivated to explore their environment and learn about the effects of their actions. The skills learned during this period of exploration are then reused to great effect later in life to solve many unfamiliar problems very quickly. This thesis presents novel methods for achieving such developmental acquisition of skill hierarchies in artificial …
Reinforcement Learning-Based Mobile Robot Navigation, Ni̇hal Altuntaş, Erkan İmal, Nahi̇t Emanet, Ceyda Nur Öztürk
Reinforcement Learning-Based Mobile Robot Navigation, Ni̇hal Altuntaş, Erkan İmal, Nahi̇t Emanet, Ceyda Nur Öztürk
Turkish Journal of Electrical Engineering and Computer Sciences
In recent decades, reinforcement learning (RL) has been widely used in different research fields ranging from psychology to computer science. The unfeasibility of sampling all possibilities for continuous-state problems and the absence of an explicit teacher make RL algorithms preferable for supervised learning in the machine learning area, as the optimal control problem has become a popular subject of research. In this study, a system is proposed to solve mobile robot navigation by opting for the most popular two RL algorithms, Sarsa($\lambda )$ and Q($\lambda )$. The proposed system, developed in MATLAB, uses state and action sets, defined in a …