  Deep Reinforcement Learning (DRL) connects the classic Reinforcement Learning
algorithms with Deep Neural Networks. A problem in DRL is that CNNs are
black-boxes and it is hard to understand the decision-making process of agents.
In order to be able to use RL agents in highly dangerous environments for
humans and machines, the developer needs a debugging tool to assure that the
agent does what is expected. Currently, rewards are primarily used to interpret
how well an agent is learning. However, this can lead to deceptive conclusions
if the agent receives more rewards by memorizing a policy and not learning to
respond to the environment. In this work, it is shown that this problem can be
recognized with the help of gradient visualization techniques. This work brings
some of the best-known visualization methods from the field of image
classification to the area of Deep Reinforcement Learning. Furthermore, two new
visualization techniques have been developed, one of which provides
particularly good results. It is being proven to what extent the algorithms can
be used in the area of Reinforcement learning. Also, the question arises on how
well the DRL algorithms can be visualized across different environments with
varying visualization techniques.
