Self-Paced Multitask Learning with Shared Knowledge

This paper introduces self-paced task selection to multitask learning, where instances from more closely related tasks are selected in a progression of easier-to-harder tasks, to emulate an effective human education strategy, but applied to multitask machine learning. We develop the mathematical foundation for the approach based on iterative selection of the most appropriate task, learning the task parameters, and updating the shared knowledge, optimizing a new bi-convex loss function. This proposed method applies quite generally, including to multitask feature learning, multitask learning with alternating structure optimization, etc. Results show that in each of the above formulations self-paced (easier-to-harder) task selection outperforms the baseline version of these methods in all the experiments

A Deep Hierarchical Approach to Lifelong Learning in Minecraft

We propose a lifelong learning system that has the ability to reuse and transfer knowledge from one task to another while efficiently retaining the previously learned knowledge-base. Knowledge is transferred by learning reusable skills to solve tasks in Minecraft, a popular video game which is an unsolved and high-dimensional lifelong learning problem. These reusable skills, which we refer to as Deep Skill Networks, are then incorporated into our novel Hierarchical Deep Reinforcement Learning Network (H-DRLN) architecture using two techniques: (1) a deep skill array and (2) skill distillation, our novel variation of policy distillation (Rusu et. al. 2015) for learning skills. Skill distillation enables the HDRLN to efficiently retain knowledge and therefore scale in lifelong learning, by accumulating knowledge and encapsulating multiple reusable skills into a single distilled network. The H-DRLN exhibits superior performance and lower learning sample complexity compared to the regular Deep Q Network (Mnih et. al. 2015) in sub-domains of Minecraft

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Використання хмарних обчислень для розвитку інформаційно-комунікаційної компетентності вчителів

The article deals with the problem for development of techers’ information and communication competence and use of cloud computing for it. The analysis of the modern approaches to the use of cloud technologies and projects for professional development of teachers and development of teachers’ information and communication competence have been presented.There are the main characteristics of software as a service on the Internet for education leading companies Google, Microsoft, IBM. There are described some actions of these companies, which are conducted to help teachers to master cloud technology for improving the professional activities and development of teachers’ information and communication competence. The examples of ways of development of teachers’ information and communication competence and training teachers to use modern ICT in the professional activity are given in the paper. The Cloud based model for development of teachers’ information and communication competence has been proposed.Стаття присвячена проблемі розвитку інформаційно-комунікаційної компетентності вчителів за допомогою використання хмарних обчислень. Здійснюється аналіз сучасних підходів до використання хмарних технологій та проектів для професійного розвитку вчителів та розвитку інформаційно-комунікаційної компетентності вчителів. Пропонуються основні характеристики хмарних обчислень провідних компаній Google, Microsoft, IBM, з точки зори їх необхідності для здійснення навчального процесу в мережі Інтернет. Описуються дії цих компаній та інші навчальні проекти, метою яких є розвиток інформаційно-комунікаційної компетентності вчителів за допомогою хмарних обчислень. Наводяться приклади шляхів рішення проблеми розвитку інформаційно-комунікаційної компетентності вчителів за допомогою використання сучасних інформаційно-комунікаційних технологій. Запропонована модель розвитку інформаційно-комунікаційної компетентності вчителів на базі хмарних обчислень, виділені основні вимоги та елементи цієї моделі