To attempt transfer learning for this scenario, the RLCI algorithm is initially run for 30 episodes on the H 8 ring with an interatomic separation of 0.9 Å. This is to ensure that the agent has learned an optimal representation of the wave function. Then, the geometry is incremented by 0.05 Å up to 1.7 Å, and the RLCI method is run for the much shorter 15 episodes. The transfer protocol explored here involves initializing the RLCI with the wave function and learned weights for the previous geometry, similar to the approach utilized in the work of Coe for the MLCI method. Over the course of computing the potential energy curve, the agent fine-tunes the previous experience to incorporate the knowledge gained elsewhere. A comparison between RLCI with and without transfer compared to the FCI results is given in Fig. . In most cases, the previous knowledge allows RLCI to obtain an improved wave function and energy in fewer iterations compared to RLCI without transfer learning. Furthermore, because the agent is able to learn on-the-fly, it appears to better handle the strong correlation as the interatomic distance increases, evidenced by the growing difference in energy between the RLCI protocols. It bears mentioning that transfer learning is problem dependent. For example, even in Fig. despite transfer learning the RLCI method with transfer does not obtain a better estimate of the wave function at . Symmetric dissociation of a H 8 ring using the STO-6G basis, with and without transferring the wave function and associated weights. In this case, the dimension of the subspace k = 120 determinants, approximately 2.5% of the full Hilbert space. The RLCI method with transfer is able to achieve smaller error with respect to FCI energies with fewer iterations for the majority of the points considered. Both dissociation curves begin at the same bond length of 0.9 Å, which is run for 30 episodes. For subsequent points along the potential energy curve, the geometry is incremented in steps of 0.05 Å and the RLCI method is run for 15 episodes. For RLCI with transfer, the wave function and learned weights from the previous geometry are used to initialize the algorithm. Otherwise, all other initializations, including the initial wave function for RLCI transfer, used a single iteration of the greedy algorithm. All other parameters are the same as those used elsewhere in the text.