Reinforcement Learning (RL) has emerged as a transformative approach in the field of autonomous systems, enabling intelligent decision making and control in robotics, self-driving cars, healthcare, industrial automation, and smart infrastructure. Throughout this discussion, we have explored the fundamental concepts, methodologies, challenges, and real world applications of RL in autonomous systems, highlighting both its potential and its limitations. The application of RL in robotics and autonomous systems is underpinned by Markov Decision Processes (MDPs), which provide a structured framework for sequential decision making. The development of value based methods, such as Deep Q Networks (DQN), and policy-based approaches, such as Policy Gradient and Actor Critic methods, has enabled robots and autonomous agents to learn complex behaviors through trial and error. Moreover, model free and model based RL techniques offer different trade offs in terms of sample efficiency and adaptability, paving the way for more versatile and practical learning based controllers.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New. Codice articolo 49999961-n
Quantità: Più di 20 disponibili
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condizione: new. Paperback. Reinforcement Learning (RL) has emerged as a transformative approach in the field of autonomous systems, enabling intelligent decision making and control in robotics, self-driving cars, healthcare, industrial automation, and smart infrastructure. Throughout this discussion, we have explored the fundamental concepts, methodologies, challenges, and real world applications of RL in autonomous systems, highlighting both its potential and its limitations. The application of RL in robotics and autonomous systems is underpinned by Markov Decision Processes (MDPs), which provide a structured framework for sequential decision making. The development of value based methods, such as Deep Q Networks (DQN), and policy-based approaches, such as Policy Gradient and Actor Critic methods, has enabled robots and autonomous agents to learn complex behaviors through trial and error. Moreover, model free and model based RL techniques offer different trade offs in terms of sample efficiency and adaptability, paving the way for more versatile and practical learning based controllers. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9786208434175
Quantità: 1 disponibili
Da: California Books, Miami, FL, U.S.A.
Condizione: New. Codice articolo I-9786208434175
Quantità: Più di 20 disponibili
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition. Codice articolo 49999961
Quantità: Più di 20 disponibili
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Condizione: As New. Unread book in perfect condition. Codice articolo 49999961
Quantità: Più di 20 disponibili
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Condizione: New. Codice articolo 49999961-n
Quantità: Più di 20 disponibili
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 100 pp. Englisch. Codice articolo 9786208434175
Quantità: 2 disponibili
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. Codice articolo 26404102672
Quantità: 4 disponibili
Da: Majestic Books, Hounslow, Regno Unito
Condizione: New. Print on Demand. Codice articolo 409051599
Quantità: 4 disponibili
Da: Biblios, Frankfurt am main, HESSE, Germania
Condizione: New. PRINT ON DEMAND. Codice articolo 18404102682
Quantità: 4 disponibili