stems must harvest energy to power operations such as circuit power consumption, transmission, and sensing. BDs need to determine when to switch between communication and energy harvesting modes but the highly dynamic nature and randomness of RF source activities make this switch operation challenging [205]. Moreover, although the BD can perform the backscat- ter and energy harvesting simultaneously, it is impractical and inefficient when the amount of harvested energy is relatively small and can only supply internal operations. Therefore, how to efficiently determine the tradeoff between energy harvesting and backscattering RF signals is critical in a dynamic environment [189]. Researchers have proposed various solutions based on RL algorithms. 10.2 ML-Based Methodologies for SS By adaptively selecting the operating mode in a fading channel environment, the through- put maximization problem of the AmBC system was solved in [205]. With the given channel distributions, the problem was modeled as an infinite-horizon Markov Decision Process (MDP) and the optimal mode switching policy was obtained by the value iteration algorithm. When the channel distribution information was unavailable, the Q-learning algorithm was employed to explore a suboptimal strategy through repeated interactions with the environment. The efficacy of their proposed Q-learning method showed that close-to-optimal throughput performance could be achieved. The authors in [189] proposed an MDP framework to determine the optimal policy for allowing the secondary transmitter to maximize system throughput. The MDP-based optimization requires complete knowledge of environmental parameters such as the probability of a channel state and the successful packet transmission ratio. To cope with these impractical constraints, a low-complexity online RL algorithm was developed that allowed the secondary transmitter to learn from its decisions to discover the optimal policy. To minimize interference, a multicluster AmBC power allocation problem was developed i