We introduce a new mathematical model of multi-agent reinforcement learning, the Multi-Agent Informational Learning Processor "MAILP" model. The model is based on the notion that agents have policies for a certain amount of information, models how this information iteratively evolves and propagates through many agents. This model is very general, and the only meaningful assumption made is that learning for individual agents progressively slows over time.

中文翻译：

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多智能体信息学习过程

我们引入了一种新的多智能体强化学习数学模型，即多智能体信息学习处理器“MAILP”模型。该模型基于代理对一定数量的信息有策略的概念，对这些信息如何通过许多代理迭代演化和传播进行建模。这个模型非常通用，唯一有意义的假设是个体代理的学习随着时间的推移逐渐变慢。