Training Reinforcement
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| Author | : Anthonie Wurth |
| Publisher | : John Wiley & Sons |
| Total Pages | : 240 |
| Release | : 2018-07-11 |
| Genre | : Business & Economics |
| ISBN | : 1119425557 |
A proven framework to fill the gap between "knowing" and "doing" Training Reinforcement offers expert guidance for more effective training outcomes. Last year, US companies spent over $165 Billion on training; while many training programs themselves provide valuable skills and concepts, even the best-designed programs are ineffective because the learned behaviors are not reinforced. Without reinforcement, learned information gets shuffled to the back of the mind in the "nice to know" file, never again to see the light of day. This book bridges the canyon between learning and doing by providing solid reinforcement strategies. Written by a former Olympic athlete and corporate training guru, this methodology works with human behavior rather than against it; you'll learn where traditional training methods fail, and how to fill those gaps with proven techniques that help training "stick." There's a difference between "telling" and "teaching," and that difference is reinforcement. Learned skills and behaviors cannot be truly effective until they are engrained, and they can only become engrained through use, encouragement, and measureable progress. This book provides a robust reinforcement framework that adds long-term value to any training program. Close the 5 Reinforcement Gaps and master the 3 Phases for results Create friction and direction while providing the perfect Push-Pull Follow the Reinforcement Flow to maintain consistency and effectiveness Create measureable behavior change by placing the participant central to the process Reinforcing training means more than simple repetition and reminders, and effective reinforcement requires a careful balance of independence and oversight. Training Reinforcement provides a ready-made blueprint with proven results, giving trainers and managers an invaluable resource for leading behavioral change.
| Author | : John G. Shedd Aquarium |
| Publisher | : |
| Total Pages | : 604 |
| Release | : 1999 |
| Genre | : Animal training |
| ISBN | : |
| Author | : Richard S. Sutton |
| Publisher | : MIT Press |
| Total Pages | : 549 |
| Release | : 2018-11-13 |
| Genre | : Computers |
| ISBN | : 0262352702 |
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.
| Author | : Alexander Zai |
| Publisher | : Manning |
| Total Pages | : 381 |
| Release | : 2020-04-28 |
| Genre | : Computers |
| ISBN | : 1617295434 |
Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap
| Author | : Larry Kay |
| Publisher | : Workman Publishing |
| Total Pages | : 305 |
| Release | : 2012-09-25 |
| Genre | : Pets |
| ISBN | : 0761168850 |
Training the Best Dog Ever, originally published in hardcover as The Love That Dog Training Program, is a book based on love and kindness. It features a program of positive reinforcement and no-fail techniques that author Dawn Sylvia-Stasiewicz used to train the White House dog, Bo Obama, and each of Senator Ted Kennedy’s dogs, among countless others. Training the Best Dog Ever relies on trust and treats, not choke collars; on bonding, not leash-yanking or reprimanding. The five-week training program takes only 10 to 20 minutes of practice a day and works both for puppies and for adult dogs that need to be trained out of bad habits. Illustrated with step-by-step photographs, the book covers hand-feeding; crate and potty training; and basic cues—sit, stay, come here—as well as more complex goals, such as bite inhibition and water safety. It shows how to avoid or correct typical behavior problems, including jumping, barking, and leash-pulling. Plus: how to make your dog comfortable in the world—a dog that knows how to behave in a vet’s office, is at ease around strangers, and more. In other words, the best dog ever.
| Author | : Katherine Bartlett |
| Publisher | : |
| Total Pages | : |
| Release | : 2018-10-22 |
| Genre | : |
| ISBN | : 9780692198537 |
| Author | : Kay Laurence |
| Publisher | : Sunshine Books (MA) |
| Total Pages | : 105 |
| Release | : 2009-01-01 |
| Genre | : Pets |
| ISBN | : 9781890948405 |
| Author | : Phil Winder Ph.D. |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 517 |
| Release | : 2020-11-06 |
| Genre | : Computers |
| ISBN | : 1492072346 |
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
| Author | : Debby McMullen |
| Publisher | : Dogwise Publishing |
| Total Pages | : 209 |
| Release | : 2010 |
| Genre | : Pets |
| ISBN | : 0976641429 |
Would you like to live in harmony in a house with multiple dogs?! It can most easily be obtained when positive reinforcement training techniques are used, along with proper living space management and good feeling habits. You'll learn how to do all those things in this book. You'll also learn techniques for adding new dogs, group training and exercise, playtime, resolving issues with problem dogs, and all the other things you need to know to guide you through your life with multiple dogs. Throughout the book there are examples of real life experiences of people using these techniques. Whether you live two dogs, six dogs, or more, How Many Dogs?! will help bring joy into your home.
| Author | : Maxim Lapan |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 547 |
| Release | : 2018-06-21 |
| Genre | : Computers |
| ISBN | : 1788839307 |
This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. Key Features Explore deep reinforcement learning (RL), from the first principles to the latest algorithms Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic algorithms Keep up with the very latest industry developments, including AI-driven chatbots Book Description Recent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google’s use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace. Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on ‘grid world’ environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots. What you will learn Understand the DL context of RL and implement complex DL models Learn the foundation of RL: Markov decision processes Evaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and others Discover how to deal with discrete and continuous action spaces in various environments Defeat Atari arcade games using the value iteration method Create your own OpenAI Gym environment to train a stock trading agent Teach your agent to play Connect4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI-driven chatbots Who this book is for Some fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL.
