  Deep reinforcement learning is a technique for solving problems in a variety
of environments, ranging from Atari video games to stock trading. This method
leverages deep neural network models to make decisions based on observations of
a given environment with the goal of maximizing a reward function that can
incorporate cost and rewards for reaching goals. With the aim of pathfinding,
reward conditions can include reaching a specified target area along with costs
for movement. In this work, multiple Deep Q-Network (DQN) agents are trained to
operate in a partially observable environment with the goal of reaching a
target zone in minimal travel time. The agent operates based on a visual
representation of its surroundings, and thus has a restricted capability to
observe the environment. A comparison between DQN, DQN-GRU, and DQN-LSTM is
performed to examine each models capabilities with two different types of
input. Through this evaluation, it is been shown that with equivalent training
and analogous model architectures, a DQN model is able to outperform its
recurrent counterparts.
