•We consider the problem of imitation learning where the examples, given by an expert, cover only a small part of a large state space. • Inverse Reinforcement Learning (IRL) provides an efficient tool for generalizing the partial demonstration, based on the assumption that the expert is maximizing an unknown utility function. • IRL consists in learning a reward function that explains the expert...

We propose a graph-based algorithm for apprenticeship learning when the reward features are noisy. Previous apprenticeship learning techniques learn a reward function by using only local state features. This can be a limitation in practice, as often some features are misspecified or subject to measurement noise. Our graphical framework, inspired from the work on Markov Random Fields, allows to ...

Much has been written already about whether the scientific machine is churning out too many PhDs and postdocs when there are a limited number of academic jobs and the competition for funding and space in competitive journals is intense. But gratifyingly, there exists a vast array of other scientific careers. We need to mentor and advise trainees about the diverse and rewarding professional oppo...

ABSTRACT COVID-19 led to the widespread withdrawal of face-to-face hospital-based clinical placements, with many medical schools switching online learning. This precipitated concern about potential negative impact on and interprofessional skill acquisition. To overcome this problem, we piloted a 12-week safe placement for 16 students at Hospital Tropical Diseases, London, during first wave pand...

Research apprenticeships offer opportunities for deep understanding of scientific practice, transparency about research careers, and possible transformational effects on precollege youth. We examined two consecutive field-based environmental biology apprenticeship programs designed to deliver realistic career exploration and connections to research scientists. The Shaw Institute for Field Train...

In this ethnographic study of summer undergraduate research (UR) experiences at four liberal arts colleges, where faculty and students work collaboratively on a project of mutual interest in an apprenticeship of authentic science research work, analysis of the accounts of faculty and student participants yields comparative insights into the structural elements of this form of UR program and its...

In apprenticeship learning we aim to learn a good policy by observing the behavior of an expert or a set of experts. In particular, we consider the case where the expert acts so as to maximize an unknown reward function defined as a linear combination of a set of state features. In this paper, we consider the setting where we observe many sample trajectories (i.e., sequences of states) but only...

We provide new theoretical results for apprenticeship learning, a variant of reinforcement learning in which the true reward function is unknown, and the goal is to perform well relative to an observed expert. We study a common approach to learning from expert demonstrations: using a classification algorithm to learn to imitate the expert’s behavior. Although this straightforward learning strat...

Against a background of the theoretical basis for the contextual approach to medical education, this paper examines and supports the changes that are occurring in undergraduate medical education throughout the world, before putting up for discussion the suggestion that the changes have not gone far enough. Consideration is given to a model of apprenticeship learning within undergraduate medical...

Human beings are the most imitative creatures in the animal kingdom. Imitation has both cognitive and social aspects and is a powerful mechanism for learning about and from people. Imitation raises theoretical questions about perception-action coupling, memory, representation, social cognition, and social affinities toward others "like me." Childhood imitation is attracting attention both withi...