A benefit of utilizing reinforcement learning is the ability for the agent to apply knowledge of gained in one domain to a related, but different, domain. This is known as transfer learning. If successful, the agent does not need to re-learn all salient aspects of the new environment, but can fine-tune the optimal actions based on experience. As a test of the potential for transfer learning in RLCI, we apply the following transfer protocol to the symmetric dissociation of a H 8 ring with the STO-6G basis.