  Q-learning is one of the most well-known Reinforcement Learning algorithms.
There have been tremendous efforts to develop this algorithm using neural
networks. Bootstrapped Deep Q-Learning Network is amongst them. It utilizes
multiple neural network heads to introduce diversity into Q-learning. Diversity
can sometimes be viewed as the amount of reasonable moves an agent can take at
a given state, analogous to the definition of the exploration ratio in RL.
Thus, the performance of Bootstrapped Deep Q-Learning Network is deeply
connected with the level of diversity within the algorithm. In the original
research, it was pointed out that a random prior could improve the performance
of the model. In this article, we further explore the possibility of replacing
priors with noise and sample the noise from a Gaussian distribution to
introduce more diversity into this algorithm. We conduct our experiment on the
Atari benchmark and compare our algorithm to both the original and other
related algorithms. The results show that our modification of the Bootstrapped
Deep Q-Learning algorithm achieves significantly higher evaluation scores
across different types of Atari games. Thus, we conclude that replacing priors
with noise can improve Bootstrapped Deep Q-Learning's performance by ensuring
the integrity of diversities.
