Training Reinforcement

2018-06-21
Training Reinforcement

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  • Author: Anthonie Wurth
  • Publisher: John Wiley & Sons
  • ISBN: 1119425565
  • Category : Business & Economics
  • Languages : en
  • Pages : 240

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.

Training Nonhuman Primates Using Positive Reinforcement Techniques

2016-02-04
Training Nonhuman Primates Using Positive Reinforcement Techniques

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  • Author: Mark J. Prescott
  • Publisher: Psychology Press
  • ISBN: 1135066329
  • Category : Psychology
  • Languages : en
  • Pages : 123

This special issue illustrates benefits to animals from positive reinforcement training (PRT) and--depending on the setting--to scientists, animal care staff, veterinarians, and in the case of the zoo, the visiting public. One important theme throughout is that training is a joint venture between human and nonhuman primate and can lead to a closer, richer relationship between the two. In summary, the editors hope this issue encourages further and wider application of PRT to primate management, care, and use, as well as aid those working with animals in applying PRT safely and effectively.

Reinforcement Learning - Principles, Concepts and Applications

2024-03-25
Reinforcement Learning - Principles, Concepts and Applications

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  • Author: Bhavatarini N
  • Publisher: MileStone Research Publications
  • ISBN: 9360130087
  • Category : Computers
  • Languages : en
  • Pages : 144

Reinforcement learning (RL) is a subfield of machine learning that deals with how an agent should learn to take actions in an environment to maximize some notion of cumulative reward. In other words, reinforcement learning is a learning paradigm where an agent learns to interact with an environment by taking actions and observing the feedback it receives in the form of rewards or penalties. It is a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. For each good action, the agent gets positive feedback, and for each bad action, the agent gets negative feedback or penalty.

Reinforcement Learning, second edition

2018-11-13
Reinforcement Learning, second edition

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  • Author: Richard S. Sutton
  • Publisher: MIT Press
  • ISBN: 0262039249
  • Category : Computers
  • Languages : en
  • Pages : 549

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.

Python Reinforcement Learning Projects

2018-09-29
Python Reinforcement Learning Projects

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  • Author: Sean Saito
  • Publisher: Packt Publishing Ltd
  • ISBN: 1788993225
  • Category : Computers
  • Languages : en
  • Pages : 287

Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries Key FeaturesImplement Q-learning and Markov models with Python and OpenAIExplore the power of TensorFlow to build self-learning modelsEight AI projects to gain confidence in building self-trained applicationsBook Description Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years. In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks. By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life. What you will learnTrain and evaluate neural networks built using TensorFlow for RLUse RL algorithms in Python and TensorFlow to solve CartPole balancingCreate deep reinforcement learning algorithms to play Atari gamesDeploy RL algorithms using OpenAI UniverseDevelop an agent to chat with humans Implement basic actor-critic algorithms for continuous controlApply advanced deep RL algorithms to games such as MinecraftAutogenerate an image classifier using RLWho this book is for Python Reinforcement Learning Projects is for data analysts, data scientists, and machine learning professionals, who have working knowledge of machine learning techniques and are looking to build better performing, automated, and optimized deep learning models. Individuals who want to work on self-learning model projects will also find this book useful.

Deep Reinforcement Learning

2022-06-10
Deep Reinforcement Learning

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  • Author: Aske Plaat
  • Publisher: Springer Nature
  • ISBN: 9811906386
  • Category : Computers
  • Languages : en
  • Pages : 414

Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects’ desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.

Hands-On Reinforcement Learning for Games

2020-01-03
Hands-On Reinforcement Learning for Games

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  • Author: Micheal Lanham
  • Publisher: Packt Publishing Ltd
  • ISBN: 1839216778
  • Category : Computers
  • Languages : en
  • Pages : 420

Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow Key FeaturesGet to grips with the different reinforcement and DRL algorithms for game developmentLearn how to implement components such as artificial agents, map and level generation, and audio generationGain insights into cutting-edge RL research and understand how it is similar to artificial general researchBook Description With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python. Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent’s productivity. As you advance, you’ll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games. By the end of this book, you’ll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications. What you will learnUnderstand how deep learning can be integrated into an RL agentExplore basic to advanced algorithms commonly used in game developmentBuild agents that can learn and solve problems in all types of environmentsTrain a Deep Q-Network (DQN) agent to solve the CartPole balancing problemDevelop game AI agents by understanding the mechanism behind complex AIIntegrate all the concepts learned into new projects or gaming agentsWho this book is for If you’re a game developer looking to implement AI techniques to build next-generation games from scratch, this book is for you. Machine learning and deep learning practitioners, and RL researchers who want to understand how to use self-learning agents in the game domain will also find this book useful. Knowledge of game development and Python programming experience are required.

Deep Reinforcement Learning with Python

2020-09-30
Deep Reinforcement Learning with Python

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  • Author: Sudharsan Ravichandiran
  • Publisher: Packt Publishing Ltd
  • ISBN: 1839215593
  • Category : Mathematics
  • Languages : en
  • Pages : 761

An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithms Key FeaturesCovers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithmLearn how to implement algorithms with code by following examples with line-by-line explanationsExplore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrationsBook Description With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects. What you will learnUnderstand core RL concepts including the methodologies, math, and codeTrain an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI GymTrain an agent to play Ms Pac-Man using a Deep Q NetworkLearn policy-based, value-based, and actor-critic methodsMaster the math behind DDPG, TD3, TRPO, PPO, and many othersExplore new avenues such as the distributional RL, meta RL, and inverse RLUse Stable Baselines to train an agent to walk and play Atari gamesWho this book is for If you’re a machine learning developer with little or no experience with neural networks interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Basic familiarity with linear algebra, calculus, and the Python programming language is required. Some experience with TensorFlow would be a plus.

Explainable and Interpretable Reinforcement Learning for Robotics

Explainable and Interpretable Reinforcement Learning for Robotics

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  • Author: Aaron M. Roth
  • Publisher: Springer Nature
  • ISBN: 3031475186
  • Category :
  • Languages : en
  • Pages : 123

Reinforcement Learning From Scratch

2022-10-27
Reinforcement Learning From Scratch

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  • Author: Uwe Lorenz
  • Publisher: Springer Nature
  • ISBN: 3031090306
  • Category : Computers
  • Languages : en
  • Pages : 195

In ancient games such as chess or go, the most brilliant players can improve by studying the strategies produced by a machine. Robotic systems practice their own movements. In arcade games, agents capable of learning reach superhuman levels within a few hours. How do these spectacular reinforcement learning algorithms work? With easy-to-understand explanations and clear examples in Java and Greenfoot, you can acquire the principles of reinforcement learning and apply them in your own intelligent agents. Greenfoot (M.Kölling, King's College London) and the hamster model (D. Bohles, University of Oldenburg) are simple but also powerful didactic tools that were developed to convey basic programming concepts. The result is an accessible introduction into machine learning that concentrates on reinforcement learning. Taking the reader through the steps of developing intelligent agents, from the very basics to advanced aspects, touching on a variety of machine learning algorithms along the way, one is allowed to play along, experiment, and add their own ideas and experiments.