Learning Control by Iterative Inversion

We propose $\textit{iterative inversion}$ -- an algorithm for learning an inverse function without input-output pairs, but only with samples from the desired output distribution and access to the forward function. The key challenge is a $\textit{distribution shift}$ between the desired outputs and the outputs of an initial random guess, and we prove that iterative inversion can steer the learning correctly, under rather strict conditions on the function. We apply iterative inversion to learn control. Our input is a set of demonstrations of desired behavior, given as video embeddings of trajectories (without actions), and our method iteratively learns to imitate trajectories generated by the current policy, perturbed by random exploration noise. Our approach does not require rewards, and only employs supervised learning, which can be easily scaled to use state-of-the-art trajectory embedding techniques and policy representations. Indeed, with a VQ-VAE embedding, and a transformer-based policy, we demonstrate non-trivial continuous control on several tasks. Further, we report an improved performance on imitating diverse behaviors compared to reward based methods.Comment: ICML 2023. Videos available at https://sites.google.com/view/iter-inve

Lymphocyte counts may predict a good response to mesenchymal stromal cells therapy in graft versus host disease patients.

Steroid-resistant GvHD is one of the most significant causes of mortality following allogeneic Hematopoietic Stem Cell Transplantation (HSCT). Treatment with mesenchymal stromal cells (MSC) seems to be a promising solution, however the results from clinical studies are still equivocal. Better selection of candidate patients and improving monitoring of patients following MSC administration can increase treatment effectiveness. In order to determine which characteristics can be used to predict a good response and better monitoring of patients, blood samples were taken prior to therapy, one week and one month after therapy, from 26 allogeneic HSCT patients whom contracted GvHD and were treated with MSCs. Samples were examined for differential blood counts, bilirubin levels and cell surface markers. Serum cytokine levels were also measured. We found that the level of lymphocytes, in particular T and NK cells, may predict a good response to therapy. A better response was observed among patients who expressed low levels of IL-6 and IL-22, Th17 related cytokines, prior to therapy. Patients with high levels of bilirubin prior to therapy showed a poorer response. The results of this study may facilitate early prediction of success or failure of the treatment, and subsequently, will improve selection of patients for MSC therapy