Abstract
This paper provides a comparative analysis between Deep Q Network (DQN)
and Double Deep Q Network (DDQN) algorithms based on their hit rate, out
of which DDQN proved to be better for Breakout game. DQN is chosen over
Basic Q learning because it understands policy learning using its neural
network which is good for complex environment and DDQN is chosen as it
solves overestimation problem (agent always choses non-optimal action
for any state just because it has maximum Q-value) occurring in basic
Q-learning.