  Multi-agent systems in which secondary agents with conflicting agendas also
alter their methods need opponent modeling. In this study, we simulate the main
agent's and secondary agents' tactics using Double Deep Q-Networks (DDQN) with
a prioritized experience replay mechanism. Then, under the opponent modeling
setup, a Mixture-of-Experts architecture is used to identify various opponent
strategy patterns. Finally, we analyze our models in two environments with
several agents. The findings indicate that the Mixture-of-Experts model, which
is based on opponent modeling, performs better than DDQN.
