Efficient Learning with Subgoals and Gaussian Process

This thesis demonstrates how data efficiency in reinforcement learning can be improved through the use of subgoals and Gaussian process. Data efficiency is extremely important in a range of problems in which gathering additional data is expensive. This tends to be the case in most problems that involve actual interactions with the physical world, such as a robot kicking a ball, an autonomous vehicle driving or a drone manoeuvring. State of the art data efficiency is achieved on several well researched problems. The systems that achieve this learn Gaussian process state transition models of the problem. The model based learner system uses the state transition model to learn the action to take in each state. The subgoal planner makes use of the state transition model to build an explicit plan to solve the problem. The subgoal planner is improved through the use of learned subgoals to aid navigation of the problem space. The resource managed learner balances the costs of computation against the value of selecting better experiments in order to improve data efficiency. An active learning system is used to estimate the value of the experiments in terms of how much they may improve the current solution. This is compared to an estimate of how much better an experiment found by expending additional computation will be along with the costs of performing that computation. A theoretical framework around the use of subgoals in problem solving is presented. This framework provides insights into when and why subgoals are effective, along with avenues for future research. This includes a detailed proposal for a system built off the subgoal theory framework intended to make full use of subgoals to create an effective reinforcement learning system

Scaling-up reinforcement learning using parallelization and symbolic planning

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The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

Proceedings of the ECCOMAS Thematic Conference on Multibody Dynamics 2015

This volume contains the full papers accepted for presentation at the ECCOMAS Thematic Conference on Multibody Dynamics 2015 held in the Barcelona School of Industrial Engineering, Universitat Politècnica de Catalunya, on June 29 - July 2, 2015. The ECCOMAS Thematic Conference on Multibody Dynamics is an international meeting held once every two years in a European country. Continuing the very successful series of past conferences that have been organized in Lisbon (2003), Madrid (2005), Milan (2007), Warsaw (2009), Brussels (2011) and Zagreb (2013); this edition will once again serve as a meeting point for the international researchers, scientists and experts from academia, research laboratories and industry working in the area of multibody dynamics. Applications are related to many fields of contemporary engineering, such as vehicle and railway systems, aeronautical and space vehicles, robotic manipulators, mechatronic and autonomous systems, smart structures, biomechanical systems and nanotechnologies. The topics of the conference include, but are not restricted to: ● Formulations and Numerical Methods ● Efficient Methods and Real-Time Applications ● Flexible Multibody Dynamics ● Contact Dynamics and Constraints ● Multiphysics and Coupled Problems ● Control and Optimization ● Software Development and Computer Technology ● Aerospace and Maritime Applications ● Biomechanics ● Railroad Vehicle Dynamics ● Road Vehicle Dynamics ● Robotics ● Benchmark ProblemsPostprint (published version