Double Deep Q-Learning in Opponent Modeling
This work addresses opponent modeling for agents in multi-agent systems, but it appears incremental as it combines existing methods like DDQN and Mixture-of-Experts.
The paper tackled the problem of opponent modeling in multi-agent systems by using Double Deep Q-Networks with prioritized experience replay and a Mixture-of-Experts architecture, finding that the Mixture-of-Experts model outperformed DDQN in performance.
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.