  Algorithmic innovation can unleash the potential of the beyond 5G (B5G)/6G
communication systems. Artificial intelligence (AI)-driven zero-touch network
slicing is envisaged as a promising cutting-edge technology to harness the full
potential of heterogeneous 6G networks and enable the automation of
demand-aware management and orchestration (MANO). The network slicing continues
towards numerous slices with micro or macro services in 6G networks, and
thereby, designing a robust, stable, and distributed learning mechanism is
considered a necessity. In this regard, robust brain-inspired and dopamine-like
learning methods, such as Actor-Critic approaches, can play a vital role. The
tutorial begins with an introduction to network slicing, reinforcement learning
(RL), and recent state-of-the-art (SoA) algorithms. Then, the paper elaborates
on the combination of value-based and policy-based methods in the form of
Actor-Critic techniques tailored to the needs of future wireless networks.
