  One of the realistic scenarios is taking a sequence of optimal actions to do
a task. Reinforcement learning is the most well-known approach to deal with
this kind of task in the machine learning community. Finding a suitable
alternative could always be an interesting and out-of-the-box matter.
Therefore, in this project, we are looking to investigate the capability of
NARS and answer the question of whether NARS has the potential to be a
substitute for RL or not. Particularly, we are making a comparison between
$Q$-Learning and ONA on some environments developed by an Open AI gym. The
source code for the experiments is publicly available in the following link:
\url{https://github.com/AliBeikmohammadi/OpenNARS-for-Applications/tree/master/misc/Python}.
