  In recent years, Multi-Agent Reinforcement Learning (MARL) has found
application in numerous areas of science and industry, such as autonomous
driving, telecommunications, and global health. Nevertheless, MARL suffers
from, for instance, an exponential growth of dimensions. Inherent properties of
quantum mechanics help to overcome these limitations, e.g., by significantly
reducing the number of trainable parameters. Previous studies have developed an
approach that uses gradient-free quantum Reinforcement Learning and
evolutionary optimization for variational quantum circuits (VQCs) to reduce the
trainable parameters and avoid barren plateaus as well as vanishing gradients.
This leads to a significantly better performance of VQCs compared to classical
neural networks with a similar number of trainable parameters and a reduction
in the number of parameters by more than 97 \% compared to similarly good
neural networks. We extend an approach of K\"olle et al. by proposing a
Gate-Based, a Layer-Based, and a Prototype-Based concept to mutate and
recombine VQCs. Our results show the best performance for mutation-only
strategies and the Gate-Based approach. In particular, we observe a
significantly better score, higher total and own collected coins, as well as a
superior own coin rate for the best agent when evaluated in the Coin Game
environment.
