This study aims to solve the problem of sparse reward and local convergence when using a reinforcement learning algorithm as controller an AUV. Based on generative adversarial imitation (GAIL) combined with multi-agent, multi-agent GAIL (MAG) is proposed. The enables AUV directly learn from expert demonstrations, overcoming difficulty slow initial training network. Parallel multi-agents reduces...

The access and integration of the massive amount of information, that can be provided by the web, can be of great help in a number of fields, including tourism and advertising of artistic sites. A “virtual visit” of a place can be a valuable experience before, during and after the experience on-site. For this reason, the contribution from the public could be merged to provide a realistic and im...

Deep generative models have recently shown great promise in imitation learning for motor control. Given enough data, even supervised approaches can do one-shot imitation learning; however, they are vulnerable to cascading failures when the agent trajectory diverges from the demonstrations. Compared to purely supervised methods, Generative Adversarial Imitation Learning (GAIL) can learn more rob...

BACKGROUND In 1989, Gail and colleagues developed a model for estimating the risk of breast cancer in women participating in a program of annual mammographic screening (designated herein as model 1). A modification of this model to project the absolute risk of developing only invasive breast cancer is referred to herein as model 2. We assessed the validity of both models by employing data from ...

This paper discusses ongoing work to build an argumentation inquiry learning system, GAIL. The purpose of GAIL is to support students in constructing scientific arguments in an undergraduate genetics course in order to facilitate deeper learning and improve argumentation skill. Students can construct argument diagrams using a dragand-drop graphical user interface. The system constructs argument...

Benichou and Gail (1995, Biometrics 51, 182-194) describe methods for estimating the absolute risk of developing disease given a set of covariate values over a specified time interval from a case-control study within a cohort. The methods are most suitable for unmatched case-control studies, and are restricted to categorical covariates. Expanding on methods for estimating relative mortality fro...