  For over a decade now, robotics and the use of artificial agents have become
a common thing.Testing the performance of new path finding or search space
optimization algorithms has also become a challenge as they require simulation
or an environment to test them.The creation of artificial environments with
artificial agents is one of the methods employed to test such algorithms.Games
have also become an environment to test them.The performance of the algorithms
can be compared by using artificial agents that will behave according to the
algorithm in the environment they are put in.The performance parameters can be,
how quickly the agent is able to differentiate between rewarding actions and
hostile actions.This can be tested by placing the agent in an environment with
different types of hurdles and the goal of the agent is to reach the farthest
by taking decisions on actions that will lead to avoiding all the obstacles.The
environment chosen is a game called "Flappy Bird".The goal of the game is to
make the bird fly through a set of pipes of random heights.The bird must go in
between these pipes and must not hit the top, the bottom, or the pipes
themselves.The actions that the bird can take are either to flap its wings or
drop down with gravity.The algorithms that are enforced on the artificial
agents are NeuroEvolution of Augmenting Topologies (NEAT) and Reinforcement
Learning.The NEAT algorithm takes an "N" initial population of artificial
agents.They follow genetic algorithms by considering an objective function,
crossover, mutation, and augmenting topologies.Reinforcement learning, on the
other hand, remembers the state, the action taken at that state, and the reward
received for the action taken using a single agent and a Deep Q-learning
Network.The performance of the NEAT algorithm improves as the initial
population of the artificial agents is increased.
