Diffusion Model-Augmented Behavioral Cloning

Imitation learning addresses the challenge of learning by observing an expert's demonstrations without access to reward signals from environments. Most existing imitation learning methods that do not require interacting with environments either model the expert distribution as the conditional probability p(a|s) (e.g., behavioral cloning, BC) or the joint probability p(s, a) (e.g., implicit behavioral cloning). Despite its simplicity, modeling the conditional probability with BC usually struggles with generalization. While modeling the joint probability can lead to improved generalization performance, the inference procedure can be time-consuming and it often suffers from manifold overfitting. This work proposes an imitation learning framework that benefits from modeling both the conditional and joint probability of the expert distribution. Our proposed diffusion model-augmented behavioral cloning (DBC) employs a diffusion model trained to model expert behaviors and learns a policy to optimize both the BC loss (conditional) and our proposed diffusion model loss (joint). DBC outperforms baselines in various continuous control tasks in navigation, robot arm manipulation, dexterous manipulation, and locomotion. We design additional experiments to verify the limitations of modeling either the conditional probability or the joint probability of the expert distribution as well as compare different generative models

Exploring Online Repeat Purchase Intentions: The Role of Habit

By focusing on online stores, this study investigates the repeat purchase intention of experienced online buyers. Prior research on online behavior continuance models perceived usefulness, trust, satisfaction, and perceived value as the major determinants of continued adoption or loyalty, overlooking the important role of habit. Building on previous work in other disciplines, we define habit in the context of online shopping as the extent to which buyers tend to shop online automatically because of learning. Using recent work on the continued usage of IS (IS continuance) and repeat purchase, we have developed a model suggesting that repeat purchase intention is not only a consequence of trust and switching cost, but also of habit. In particular, in our research model, we propose that online shopping habit moderate the influence of trust such that its importance in determining repeat purchase intention decreases as the online shopping behavior takes on a more habitual nature. Integrating prior research on habit, IS continuance, and repeat purchase further, we suggest how antecedents of repeat purchase intention relate to drivers of habitualization. Data collected from 462 of Yahoo!Kimo shopping center’s customers provide strong support for the research model. Results indicate that higher level of habit deflated trust’s effect on repeat purchase intention. The data also show that satisfaction and familiarity are key to habit formation and thus relevant in the context of online repeat purchase