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"Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade.The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. "

"Natural fiber-reinforced composites have the potential to replace synthetic composites, leading to less expensive, stronger and more environmentally-friendly materials. This book provides a detailed review on how a broad range of biofibers can be used as reinforcements in composites and assesses their overall performance.The book is divided into five major parts according to the origins of the different biofibers. Part I contains chapters on bast fibers, part II leaf fibers, part III seed fibers, part IV grass, reed and cane fibers, and finally part V covers wood, cellulosic and other fibers including cellulosic nanofibers. Each chapter reviews a specific type of biofiber providing detailed information on the sources of each fiber, their cultivation, how to process and prepare them, and how to integrate them into composite materials. The chapters outline current and potential applications for each fiber and discuss their main strengths and weaknesses."

"This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme.There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent projects with big data (e.g., AlphaGo), and gets quite good results."

"Water permeability of plain and fiber reinforced concrete was measured with and without and applied compressive stress. For the measurement of permeability under stress , a novel test technique was developed, in this technique , two hollow core concrete cylinders were simultaneously tested -one with stress and the other without using indentical flow conditions. A special design of the permeability cell eliminates leakage and allows the specimen to achieve conditions of flow equilibrium early in the test. For the stressed specimens , two levels of the applied stress , 0.3 Fu and 0.5 fu where fu is the ultimate strength of concrete in compression were investigated. A collated cellulose fiber at volume fractions of 0.1, 0.3 and 0.5% was used. Results indicated that in the unstressed state, fiber reinforcement reduces the permeability of concrete. For the stressed concrete on the other hand, and interesting phenomenon was observed. Initially as the applied stress was increased , a reduction in the permeability for both plain and fiber reinforced concrete was observed. This reduction, however , occurred only to a certain threshold value of stress . Beyond this threshold, a rapid increase in the permeability occurred for plain concrete. For fiber reinforced concrete as well, an increase in the permeability was noticed beyond the threshold value of stress, but the permeability still remained below the unstressed level."

"Penelitian ini menyelidiki efektivitas audience effect dan positive reinforcement terhadap tugas berbasis waktu yang diukur dalam waktu penyelesaian. Partisipan dalam percobaan ini adalah 30 mahasiswa sarjana di University of Queensland. Penelitian ini adalah penelitian eksperimen kelompok independen menggunakan desain 3 tingkat yang mengalokasikan peserta ke dalam tiga kelompok berbeda: peserta melakukan tugas tanpa penonton, dengan penonton, dan dengan penonton disertai dengan positive reinforcement yang diterapkan. Hasil penelitian ini pada jangka waktu dalam menyelesaikan tugas yang diberikan (50-piece puzzle) sebagai dependent variable menunjukkan hasil yang tidak signifikan. Hasil ini tidak mendukung temuan sebelumnya yang menunjukkan bahwa positive reinforcement ditemukan untuk memberikan efek terbalik di mana hal itu menyebabkan peserta untuk melakukan tugas lebih lama.The present study investigates the effectiveness of audience effect and positive reinforcement towards a time-based task measured in completion time. Participants in this experiment were 30 undergraduate University of Queensland students. The study is an independent groups experiment research using a 3-level design which allocates the participants into three different group: participants undertaking the task without audience, with an audience, and with an audience accompanied by positive reinforcement being applied. The result on the time taken to complete the given task (50-piece puzzle) as the dependent variable showed a non-significant result. These results do not support previous findings and showed contradicting findings that positive reinforcement was found to give a reversed effect where it causes participants to do the task longer."