Akademik Veri Yönetim Sistemi
8th International Conference on Advances in Statistics, Gazimagusa, Kıbrıs (Kktc), 16 Mayıs - 18 Haziran 2022, ss.29
Abstract
Reinforcement
learning (RL) is currently one of the most dynamic areas in Artificial
Intelligence research. It is the technique in which an agent learns how to
obtain rewards through interactions with their environment. Some real-world
applications, such as robotics and autonomous, are particularly suitable for
the RL approach as the environment is unknown and the consequences of actions
are uncertain. Because many Reinforced Learning examples in the literature are
trained with simple and intuitive reward functions, it struggles with sparse
and deceptive feedback. In addition, it may be necessary to search for
different ways for trial and error and sometimes return to the previous steps
after discovering these ways, so it is important to use the right search
algorithms. Discovering all paths in remains one of the challenges of the
field. In Reinforced Learning, it may not be the optimum solution to give high
values to the activities we want to take place or to apply a penalty system to
undesirable activities. However, there is no proven method that selects the
appropriate reward function for every situation. The reward functions used are
intuitive. In this study, it is planned to train the Reinforced Learning algorithm
in a game environment by giving different reward functions. It is aimed to
examine the performances and strategies of the learning algorithms and to make comparisons between them. However, for
this, it is necessary to have environments where we can directly access the
situations and actions in the game, and the diversity of decisions that lead to
the result in the game for the reward function. DOOM 2
game is suitable for this study is found by examining the environments in the
literature. In this game there are multiple chapters and different obstacles
that will ensure that the chapter designs do not reach the goal directly. It is
aimed to compare the effects of department diversity and barriers on education,
as well as to examine the performance and strategies of representatives trained
in the same departments with different reward functions and to make comparisons
between them.
Key Words: deep learning; reinforcement learning; video games; deep q
learning