Generative adversarial imitation learning (GAIL) has attracted increasing attention in the field of robot learning. It enables robots to learn a policy to achieve a task demonstrated by an expert while simultaneously estimating the reward function behind the expert's behaviors. However, this framework is limited to learning a single task with a single reward function. This study proposes an extended framework called situated GAIL (S-GAIL), in which a task variable is introduced to both the discriminator and generator of the GAIL framework. The task variable has the roles of discriminating different contexts and making the framework learn different reward functions and policies for multiple tasks. To achieve the early convergence of learning and robustness during reward estimation, we introduce a term to adjust the entropy regularization coefficient in the generator's objective function. Our experiments using two setups (navigation in a discrete grid world and arm reaching in a continuous space) demonstrate that the proposed framework can acquire multiple reward functions and policies more effectively than existing frameworks. The task variable enables our framework to differentiate contexts while sharing common knowledge among multiple tasks.

The goal of this case study is to explore storm events in various locations in the United States and analyze the frequency and damage costs associated with different types of events. A machine learning model is used to predict the damage costs, based on historical data from 1980 - 2018. The calculations are then performed in an app, which can be shared as a web application. This example also highlights techniques for preprocessing data in various forms (numeric, text, categorical, dates and times) and working with large data sets which do not fit into memory. The example is used in the "Data Science with MATLAB" webinar series.

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I'm answering questions that AI/ML/CV people not familiar with graphs or graph neural networks typically ask. I provide PyTorch examples to clarify the idea behind this relatively new and exciting kind of model. To answer them, I'll provide motivating examples, papers and Python code making it a tutorial on Graph Neural Networks (GNNs). Some basic knowledge of machine learning and computer vision is expected, however, I'll provide some background and intuitive explanation as we go. First of all, let's briefly recall what is a graph?

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You can see the tensorflow 2.0 roadmap and overall features of tensorflow 2.0. We hope you like them.

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Chatbots can be used for several purposes, such as helping customers and answering complex FAQs. They have even been used to help pick candidates in recruitment processes, so it is no surprise that the educational system is trying to implement chatbots. The scopes of application could advance administration with the aim of facilitating procedures, as a date reminder, assistance in the reinforcement of educational content and mentoring and accompaniment actions. Properly trained with a huge quantity of data, a chatbot could ease both the educational process of the student and the tasks of the teacher. This artificial assistant could respond to a 24/7 demand, allowing professors to take care of the most qualitative tasks.