Complex learned behaviors involve the integrated action of distributed brain circuits. While the contributions of individual regions to learning have been extensively investigated, understanding how distributed brain networks orchestrate their activity over the course of learning remains elusive. To address this gap, we used fMRI combined with tools from dynamic network neuroscience to obtain time-resolved descriptions of network coordination during reinforcement learning. We found that reinforcement learning involves dynamic changes in network coupling between the striatum and distributed brain networks. Moreover, we found that the degree of flexibility in whole-brain circuit dynamics correlates with participants learning rate, as derived from reinforcement learning models. Finally, we found that episodic memory, measured in the same participants at the same time, was related to dynamic connectivity in distinct brain networks. These results support the idea that dynamic changes in network communication provide a mechanism for information integration during reinforcement learning.