论文标题
人工智能竞争团队中异质策略的自然出现
Natural Emergence of Heterogeneous Strategies in Artificially Intelligent Competitive Teams
论文作者
论文摘要
混合合作竞争环境中的多代理策略可能很难手工制作，因为每个代理商都需要在与对手竞争的同时与队友进行协调。基于学习的算法很有吸引力，但是许多场景都需要非均质的代理行为才能使团队的成功，这增加了学习算法的复杂性。在这项工作中，我们开发了一个名为Fortattack的竞争性多代理环境，其中两个团队相互竞争。我们证实了用图神经网络对代理进行建模并通过强化学习训练它们的方法，从而导致每个团队日益复杂的策略的演变。当这种行为可以导致团队的成功时，我们观察到同质代理人中异质行为的自然出现。均质剂的这种异质行为很有吸引力，因为任何代理都可以在测试时替代其他药物的角色。最后，我们提出了合奏培训，我们利用进化的对手策略为友好的代理商培训一项政策。
Multi agent strategies in mixed cooperative-competitive environments can be hard to craft by hand because each agent needs to coordinate with its teammates while competing with its opponents. Learning based algorithms are appealing but many scenarios require heterogeneous agent behavior for the team's success and this increases the complexity of the learning algorithm. In this work, we develop a competitive multi agent environment called FortAttack in which two teams compete against each other. We corroborate that modeling agents with Graph Neural Networks and training them with Reinforcement Learning leads to the evolution of increasingly complex strategies for each team. We observe a natural emergence of heterogeneous behavior amongst homogeneous agents when such behavior can lead to the team's success. Such heterogeneous behavior from homogeneous agents is appealing because any agent can replace the role of another agent at test time. Finally, we propose ensemble training, in which we utilize the evolved opponent strategies to train a single policy for friendly agents.
