  Cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising
technique for achieving high spectral efficiency (SE) using multiple
distributed access points (APs). However, harsh propagation environments often
lead to significant communication performance degradation due to high
penetration loss. To overcome this issue, we introduce the reconfigurable
intelligent surface (RIS) into the CF mMIMO system as a low-cost and
power-efficient solution. In this paper, we focus on optimizing the joint
precoding design of the RIS-aided CF mMIMO system to maximize the sum SE. This
involves optimizing the precoding matrix at the APs and the reflection
coefficients at the RIS. To tackle this problem, we propose a fully distributed
multi-agent reinforcement learning (MARL) algorithm that incorporates fuzzy
logic (FL). Unlike conventional approaches that rely on alternating
optimization techniques, our FL-based MARL algorithm only requires local
channel state information, which reduces the need for high backhaul capacity.
Simulation results demonstrate that our proposed FL-MARL algorithm effectively
reduces computational complexity while achieving similar performance as
conventional MARL methods.
