The ability to model the mental states of others is crucial to human social intelligence, and can offer similar benefits to artificial agents with respect to the social dynamics induced in multi-agent settings. We present a method of grounding semantically meaningful, human-interpretable beliefs within policies modeled by deep networks. We then consider the task of 2nd-order belief prediction. We propose that ability of each agent to predict the beliefs of the other agents can be used as an intrinsic reward signal for multi-agent reinforcement learning. Finally, we present preliminary empirical results in a mixed cooperative-competitive environment.

翻译：理解他人心理状态的能力对人类社交智能至关重要，对于多智能体环境中的人工智能体而言，这种能力同样能在社交动态方面带来益处。我们提出一种方法，将语义上有意义、人类可解释的信念嵌入到深度网络建模的策略中。随后，我们考虑二阶信念预测任务，并主张每个智能体预测其他智能体信念的能力可作为多智能体强化学习的内在奖励信号。最后，我们展示了在混合合作-竞争环境中的初步实证结果。