Multi-agent reinforcement learning (MARL) is well-suited for runtime decision-making in optimizing the performance of systems where multiple agents coexist and compete for shared resources. However, applying common deep learning-based MARL solutions to real-world problems suffers from issues of interpretability, sample efficiency, partial observability, etc. To address these challenges, we present an event-driven formulation, where decision-making is handled by distributed co-operative MARL agents using neuro-symbolic methods. The recently introduced neuro-symbolic Logical Neural Networks (LNN) framework serves as a function approximator for the RL, to train a rules-based policy that is both logical and interpretable by construction. To enable decision-making under uncertainty and partial observability, we developed a novel probabilistic neuro-symbolic framework, Probabilistic Logical Neural Networks (PLNN), which combines the capabilities of logical reasoning with probabilistic graphical models. In PLNN, the upward/downward inference strategy, inherited from LNN, is coupled with belief bounds by setting the activation function for the logical operator associated with each neural network node to a probability-respecting generalization of the Fr\'echet inequalities. These PLNN nodes form the unifying element that combines probabilistic logic and Bayes Nets, permitting inference for variables with unobserved states. We demonstrate our contributions by addressing key MARL challenges for power sharing in a system-on-chip application.

多智能体强化学习（MARL）在优化多智能体共享资源中的系统性能方面具有潜力，但常见的深度学习MARL解决方案在真实世界问题中存在可解释性、样本效率、部分可观察性等问题。为了解决这些挑战，我们提出了一种基于事件驱动的公式，利用神经符号方法处理分布式协作MARL智能体的决策制定。我们还开发了一种新颖的概率神经符号框架，概率逻辑神经网络（PLNN），将逻辑推理能力与概率图模型相结合，以实现在不确定性和部分可观察性下的决策制定。我们通过解决片上系统中的功率共享问题来展示我们的研究成果。

神经符号方法在可解释性和概率决策中的多智能体强化学习