Considerable evidence highlights the dorsolateral prefrontal cortex (DLPFC) as a key region for hierarchical learning. In a previous electroencephalography (EEG) study we found that the low level precision weighted prediction errors (pwPEs) were encoded by frontal theta oscillations, centered on F4 corresponding to right DLPFC (rDLPFC). However, the causal relationship between frontal theta oscillations and hierarchical learning remains poorly understood. To investigate this question, in the current study, participants received theta (6Hz) and sham high-definition transcranial alternating current stimulation over the rDLPFC, while performing the probabilistic reversal learning task. Behaviorally, theta tACS induced a significant reduction in accuracy for the stable environment, but not for the volatile environment, relative to the sham condition. Computationally, we implemented a combination of a hierarchical Bayesian learning and a decision model. Theta tACS induced a significant increase in low level (i.e., probability) learning rate and uncertainty of low level estimation relative to sham condition. Instead, the temperature parameter of the decision model was not significantly altered due to theta stimulation. These results indicate that theta frequency may modulate the (low level) learning rate. Furthermore, environmental features (e.g., its stability) may determine whether learning is optimized as a result.