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Products related to Reinforcement:

  • Deep Reinforcement Learning
    Deep Reinforcement Learning
    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.
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  • The Silent Language of Psychotherapy : Social Reinforcement of Unconscious Processes
    The Silent Language of Psychotherapy : Social Reinforcement of Unconscious Processes
    Therapeutic changes occur in many places, and among animals as well as humans.A theory that attempts to explain therapeutic changes should be based on principles that apply not only to those changes occurring during the hour, but also to those observed in the educational process, in interpersonal relations, and in the social milieu, as well as with animals.It would be desirable to discover principles broad enough to provide a deeper understanding of therapeutic change in this wide variety of situations. Experienced therapists appear to be similar in what they are doing, although they may disagree as to why they do it.In spite of the arguments about theoretical formulations, it can be observed that during an hour with a patient many psychotherapists may not follow their own theories too well.There probably is some relevance in a comparison of psychotherapy with a concept formation test: the subject gives the correct answers but is unable to state why he did so or what principles he followed in making his choices.The therapist, too, may help a patient but he is often uncertain as to "why" and "how." It is very likely that the effective principles in therapeutic work rest on processes that are more general than the specific principles advanced by different schools. This volume combines the elements of psychodynamic and cognitive behavioral therapy in a theoretical system that focuses on the importance of patient-therapist interaction, especially in terms of the exchange of subtle or covert communication cues.In this significantly updated and expanded edition of their classic text, Beier and Young analyze recent developments in new areas of practice facing today's therapist: managed care and the clinical impact of the control of healthcare delivery; and biological intervention and other issues related to psychotropic medication.
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  • Grokking Deep Reinforcement Learning
    Grokking Deep Reinforcement Learning
    Written for developers with some understanding of deep learning algorithms.Experience with reinforcement learning is not required. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching.You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. We all learn through trial and error. We avoid the things that cause us to experience pain and failure.We embrace and build on the things that give us reward and success.This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. • Foundational reinforcement learning concepts and methods • The most popular deep reinforcement learning agents solving high-dimensional environments • Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior on their own from raw sensory input.The system perceives the environment, interprets the results of its past decisions and uses this information to optimize its behavior for maximum long-term return.
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  • Fundamentals of Reinforcement Learning
    Fundamentals of Reinforcement Learning
    Artificial intelligence (AI) applications bring agility and modernity to our lives, and the reinforcement learning technique is at the forefront of this technology.It can outperform human competitors in strategy games, creative compositing, and autonomous movement.Moreover, it is just starting to transform our civilization. This book provides an introduction to AI, specifies machine learning techniques, and explores various aspects of reinforcement learning, approaching the latest concepts in a didactic and illustrated manner.It is aimed at students who want to be part of technological advances and professors engaged in the development of innovative applications, helping with academic and industrial challenges. Understanding the Fundamentals of Reinforcement Learning will allow you to:Understand essential AI conceptsGain professional experienceInterpret sequential decision problems and solve them with reinforcement learningLearn how the Q-Learning algorithm worksPractice with commented Python codeFind advantageous directions
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  • What is the difference between continuous reinforcement and intermittent reinforcement?

    Continuous reinforcement involves providing a reward every time a specific behavior occurs, while intermittent reinforcement involves providing a reward only some of the time a behavior occurs. Continuous reinforcement tends to lead to faster learning and more consistent behavior, but it can also lead to quicker extinction of the behavior if the reward is removed. Intermittent reinforcement, on the other hand, can lead to more persistent behavior because the individual is unsure when the reward will come, making them more likely to continue the behavior in hopes of receiving a reward.

  • What is electric sound reinforcement?

    Electric sound reinforcement is the use of electronic amplification and processing equipment to enhance and distribute sound in a live performance or public speaking setting. This typically involves microphones to capture sound, amplifiers to increase the volume, and speakers to project the sound to the audience. Sound reinforcement systems can also include signal processing equipment such as equalizers and compressors to shape and control the sound. Overall, electric sound reinforcement allows for the amplification and distribution of sound to ensure that it is clearly heard by the audience.

  • What is reinforcement in model building?

    Reinforcement in model building refers to the process of providing feedback to a model based on its performance. This feedback is used to adjust the model's parameters in order to improve its accuracy and predictive power. Reinforcement can involve rewarding the model for correct predictions and penalizing it for incorrect predictions, helping the model learn and adapt over time. This iterative process of reinforcement is crucial in training machine learning models to make accurate predictions and decisions.

  • How do you drill reinforcement steel?

    To drill reinforcement steel, you will need a high-quality drill with a sharp masonry bit specifically designed for drilling through steel. It's important to use a slow speed and apply steady pressure to avoid overheating the drill bit and damaging the steel. Additionally, using a lubricant such as cutting oil can help to reduce friction and prolong the life of the drill bit. It's also important to wear appropriate safety gear, such as goggles and gloves, when drilling reinforcement steel to protect yourself from any metal shards or debris.

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  • ESL - Reinforcement Activity Book
    ESL - Reinforcement Activity Book
    Price: 12.95 £ | Shipping*: 3.99 £
  • Control Systems and Reinforcement Learning
    Control Systems and Reinforcement Learning
    A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails.This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra.A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails.Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning.Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control.These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.
    Price: 49.99 £ | Shipping*: 0.00 £
  • Deep Reinforcement Learning in Action
    Deep Reinforcement Learning in Action
    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. Key features • Structuring problems as Markov Decision Processes • Popular algorithms such Deep Q-Networks, Policy Gradient method and Evolutionary Algorithms and the intuitions that drive them • Applying reinforcement learning algorithms to real-world problems Audience You’ll need intermediate Python skills and a basic understanding of deep learning. About the technology Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior from their own raw sensory input.The system perceives the environment, interprets the results of its past decisions, and uses this information to optimize its behavior for maximum long-term return.Deep reinforcement learning famously contributed to the success of AlphaGo but that’s not all it can do! Alexander Zai is a Machine Learning Engineer at Amazon AI working on MXNet that powers a suite of AWS machine learning products.Brandon Brown is a Machine Learning and Data Analysis blogger at outlace.com committed to providing clear teaching on difficult topics for newcomers.
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  • Plastics Reinforcement and Industrial Applications
    Plastics Reinforcement and Industrial Applications
    When combined with reinforcing agents, plastics can be used for a number of high-temperature applications.Plastics Reinforcement and Industrial Applications provides a detailed discussion on plastics, polymers, and reinforcing agents (including organic and natural biomaterials).Focused specifically on improving the mechanical, thermal, and electrical stability of plastics by combining them with reinforcing agents, this book explains the background of reinforced plastics and describes how they work.The book examines reinforcing agents that include glass fibers, carbon fibers, carbon nanotubes, graphite, talc, and minerals, and commonly used plastics such as polyamides, polyesters, polyethylene terephthalate, and epoxy resins.It also introduces newer plastics such as polyimides, polysulfones, polyethersulfone, polyphenylene sulfide, and polyether ether ketones.It highlights recent developments in the field that include the use of nanocomposites for manufacturing sports equipment, and other applications of nanoparticles in polymer reinforcement.In addition, use of this material can aid in the production of plastics utilized in the construction of aircraft and light weight automobiles.The author covers a wide range of applications that may be applied in general engineering, automotive, aerospace, building materials, electronics and microelectronics, power sources, medical, and bioengineering.He also includes material on natural fibers used for reinforcement and green chemistry applications. Suitable for use in the metals and plastics industries, Plastics Reinforcement and Industrial Applications is an ideal resource for polymer and material scientists, and chemical and mechanical engineers.
    Price: 175.00 £ | Shipping*: 0.00 £

  • What is meant by reinforcement loops?

    Reinforcement loops refer to a process in which a certain action or behavior is repeated and strengthened through feedback or consequences. This can be positive reinforcement, where a desirable behavior is rewarded, or negative reinforcement, where an undesirable behavior is punished. The loop aspect comes from the fact that the consequences of the behavior then influence the likelihood of that behavior occurring again in the future, creating a cycle of reinforcement. This concept is often used in psychology and behavioral science to understand and modify human behavior.

  • What is a reinforcement in stockings?

    A reinforcement in stockings refers to a section of the stocking that is designed to provide extra durability and support in areas that are prone to wear and tear, such as the toes, heels, and soles. This reinforcement is often made of a thicker or stronger material, such as nylon or lycra, to prevent runs and prolong the life of the stockings. It helps to maintain the shape and integrity of the stocking, making it more resistant to damage from everyday use.

  • What does reinforcement mean in pedagogy?

    Reinforcement in pedagogy refers to the use of positive or negative consequences to strengthen a student's behavior. Positive reinforcement involves providing rewards or praise to encourage desired behaviors, while negative reinforcement involves removing an unpleasant stimulus to encourage the same. Both types of reinforcement are used to increase the likelihood that a student will continue to exhibit the desired behavior in the future. Reinforcement is a key component of behaviorist theories of learning and is widely used in educational settings to shape and maintain student behavior.

  • What is the difference between social communication and social interaction?

    Social communication refers to the exchange of information through verbal and nonverbal means, such as speaking, gestures, and facial expressions, to convey thoughts, feelings, and ideas. On the other hand, social interaction involves the broader concept of engaging with others in a social setting, including activities such as playing, sharing, and collaborating. While social communication focuses on the exchange of messages, social interaction encompasses the overall dynamic of how individuals engage with one another in social contexts.

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