  Recent times have witnessed sharp improvements in reinforcement learning
tasks using deep reinforcement learning techniques like Deep Q Networks, Policy
Gradients, Actor Critic methods which are based on deep learning based models
and back-propagation of gradients to train such models. An active area of
research in reinforcement learning is about training agents to play complex
video games, which so far has been something accomplished only by human
intelligence. Some state of the art performances in video game playing using
deep reinforcement learning are obtained by processing the sequence of frames
from video games, passing them through a convolutional network to obtain
features and then using recurrent neural networks to figure out the action
leading to optimal rewards. The recurrent neural network will learn to extract
the meaningful signal out of the sequence of such features. In this work, we
propose a method utilizing a transformer network which have recently replaced
RNNs in Natural Language Processing (NLP), and perform experiments to compare
with existing methods.
