The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Humans have a natural instinct to help others. Imagine walking up to a stranger on the subway and asking them for their seat. What about asking a random person on the street if you could borrow their phone? If the idea makes you squeamish, you're not alone--social psychologists have found that doing these very things makes most of us almost unbearably uncomfortable. But here's the funny thing: even though we hate to ask for help, most people are wired to be helpful. And that's a good thing, because every day in the modern, uber-collaborative workplace, we all need to know when and how to call in the cavalry. However, asking people for help isn't intuitive; in fact, a lot of our instincts are wrong. As a result, we do a poor job of calling in the reinforcements we need, leaving confused or even offended colleagues in our wake. This pragmatic book explains how to get it right. With humor, insight, and engaging storytelling, Heidi Grant, PhD, describes how to elicit helpful behavior from your friends, family, and colleagues--in a way that leaves them feeling genuinely happy to lend a hand. Whether you're a first-time manager or a seasoned leader, getting people to pitch in is what leadership is. Fortunately, people have a natural instinct to help other human beings; you just need to know how to channel this urge into what it is you specifically need them to do. It's not manipulation. It's just management.

The contingent relationship between actions and their consequences lies at the heart of Skinner’s experimental analysis of behavior. Particular patterns of behavior emerge depending upon the contingencies established. Ferster and Skinner examined the effects of different schedules of reinforcement on behavior. An extraordinary work, Schedules of Reinforcement represents over 70,000 hours of research primarily with pigeons, though the principles have now been experimentally verified with many species including human beings. At first glance, the book appears to be an atlas of schedules. And so it is, the most exhaustive in existence. But it is also a reminder of the power of describing and explaining behavior through an analysis of measurable and manipulative behavior-environment relations without appealing to physiological mechanisms in the brain. As en exemplar and source for the further study of behavioral phenomena, the book illustrates the scientific philosophy that Skinner and Ferster adopted: that a science is best built from the ground up, from a firm foundation of facts that can eventually be summarized as scientific laws.

Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website

This Book Systematically Explains The Basic Principles And Techniques Involved In The Design Of Reinforced Concrete Structures. It Exhaustively Covers The First Course On The Subject At B.E./ B.Tech Level.Important Features: * Exposition Is Based On The Latest Indian Standard Code Is: 456-2000. * Limit State Method Emphasized Throughout The Book. * Working Stress Method Also Explained. * Detailing Aspects Of Reinforcement Highlighted. * Incorporates Earthquake Resistant Design. * Includes A Large Number Of Solved Examples, Practice Problems And Illustrations.The Book Would Serve As A Comprehensive Text For Undergraduate Civil Engineering Students. Practising Engineers Would Also Find It A Valuable Reference Source.