  Deep Neural Networks have achieved great success in some of the complex tasks
that humans can do with ease. These include image recognition/classification,
natural language processing, game playing etc. However, modern Neural Networks
fail or perform poorly when trained on tasks that can be solved easily using
backtracking and traditional algorithms. Therefore, we use the architecture of
the Neuro Logic Machine (NLM) and extend its functionality to solve a 9X9 game
of Sudoku. To expand the application of NLMs, we generate a random grid of
cells from a dataset of solved games and assign up to 10 new empty cells. The
goal of the game is then to find a target value ranging from 1 to 9 and fill in
the remaining empty cells while maintaining a valid configuration. In our
study, we showcase an NLM which is capable of obtaining 100% accuracy for
solving a Sudoku with empty cells ranging from 3 to 10. The purpose of this
study is to demonstrate that NLMs can also be used for solving complex problems
and games like Sudoku. We also analyze the behaviour of NLMs with a
backtracking algorithm by comparing the convergence time using a graph plot on
the same problem. With this study we show that Neural Logic Machines can be
trained on the tasks that traditional Deep Learning architectures fail using
Reinforcement Learning. We also aim to propose the importance of symbolic
learning in explaining the systematicity in the hybrid model of NLMs.
