Bachelors project Pacman implementation using reinforcement learning, shows
the reason and motivation that made me choose this project. In bachelors
project we went over theoretical principals of Reinforcement learning algorithms, where we explained theoretical background of Q-learning and Deep
Q-learning which we implemented and used on a game Pacman. Our approach was special because of our comparison of success between these two
algorithms which were implemented on a game with restricted ability to impact on the movement and decision of our agent. Based on the accumulated
knowledge in the course of our project we implemented both algorithms, that
when finished returned some interesting results. Throughout our implementation we experienced a lot of challenges, some more fun than others, but
in the end we successfully resolved all of them. Based on gathered results
we found out that despite restricted movement of our agent, the algorithms
were in average approximately as good or in some cases drastically better
than average amateur Pacman players.
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