论文标题
对手建模的双重Q学习
Double Deep Q-Learning in Opponent Modeling
论文作者
论文摘要
具有冲突议程的二级代理也改变其方法的多代理系统需要对手建模。在这项研究中，我们使用双重Q-Networks（DDQN）模拟了主要代理商和二级代理的策略，并具有优先的经验重播机制。然后，在对手建模设置下，使用架构的混合物来识别各种对手策略模式。最后，我们在两个环境中分析了我们的模型。研究结果表明，基于对手建模的Experts模型的性能优于DDQN。
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.
