Learning Reinforcement Markov Decision (18 results)

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Bachelor Thesis from the year 2021 in the subject Engineering - Computer Engineering, grade: 1,3, Hamburg University of Technology (Embedded Systems), language: English, abstract: This bachelor thesis aims to illustrate the idea behind Markov Decision Processes (MDP) and to present a few basic methods of Reinforcement Learning (RL) namely Monte Carlo Learning and Q-Learning, which are the solutions for decision problems modelled by MDPs. For the last section we apply these methods on an application and in the end discuss the results.Let us imagine the scenario where we put a hamster inside a maze, we expect the hamster to go through the maze till it reaches some point we considered as the goal. Well, it may randomly work but most of the time it won't. At this place, the hamster does not know how important this particular point remains namely the goal. But how will it be, when we remunerate the hamster once the goal is reached, he receives a reward for example a piece of cheese. The hamster will start to remember the route, which leads to the cheese and he maybe will learn to go the easy and quick way to achieve this goal. What we did, is that we reinforce the good behavior of the hamster by giving it some reward.…

Hardcover. Condition: new. Hardcover. This book offers a structured exploration of how Markov Decision Processes (MDPs) and Deep Reinforcement Learning (DRL) can be used to model and optimize UAV-assisted Internet of Things (IoT) networks, with a focus on minimizing the Age of Information (AoI) during data collection. Adopting a tutorial-style approach, it bridges theoretical models and practical algorithms for real-time decision-making in tasks like UAV trajectory planning, sensor transmission scheduling, and energy-efficient data gathering. Applications span precision agriculture, environmental monitoring, smart cities, and emergency response, showcasing the adaptability of DRL in UAV-based IoT systems. Designed as a foundational reference, it is ideal for researchers and engineers aiming to deepen their understanding of adaptive UAV planning across diverse IoT applications. This book offers a structured exploration of how Markov Decision Processes (MDPs) and Deep Reinforcement Learning (DRL) can be used to model and optimize UAV-assisted Internet of Things (IoT) networks, with a focus on minimizing the Age of Information (AoI) during data collection. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Hardcover. Condition: Brand New. 125 pages. 9.25x6.10x9.49 inches. In Stock.

Hardcover. Condition: new. Hardcover. This book offers a structured exploration of how Markov Decision Processes (MDPs) and Deep Reinforcement Learning (DRL) can be used to model and optimize UAV-assisted Internet of Things (IoT) networks, with a focus on minimizing the Age of Information (AoI) during data collection. Adopting a tutorial-style approach, it bridges theoretical models and practical algorithms for real-time decision-making in tasks like UAV trajectory planning, sensor transmission scheduling, and energy-efficient data gathering. Applications span precision agriculture, environmental monitoring, smart cities, and emergency response, showcasing the adaptability of DRL in UAV-based IoT systems. Designed as a foundational reference, it is ideal for researchers and engineers aiming to deepen their understanding of adaptive UAV planning across diverse IoT applications. This book offers a structured exploration of how Markov Decision Processes (MDPs) and Deep Reinforcement Learning (DRL) can be used to model and optimize UAV-assisted Internet of Things (IoT) networks, with a focus on minimizing the Age of Information (AoI) during data collection. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers a structured exploration of how Markov Decision Processes (MDPs) and Deep Reinforcement Learning (DRL) can be used to model and optimize UAV-assisted Internet of Things (IoT) networks, with a focus on minimizing the Age of Information (AoI) during data collection. Adopting a tutorial-style approach, it bridges theoretical models and practical algorithms for real-time decision-making in tasks like UAV trajectory planning, sensor transmission scheduling, and energy-efficient data gathering. Applications span precision agriculture, environmental monitoring, smart cities, and emergency response, showcasing the adaptability of DRL in UAV-based IoT systems. Designed as a foundational reference, it is ideal for researchers and engineers aiming to deepen their understanding of adaptive UAV planning across diverse IoT applications.…

Hardcover. Condition: new. Hardcover. This book offers a structured exploration of how Markov Decision Processes (MDPs) and Deep Reinforcement Learning (DRL) can be used to model and optimize UAV-assisted Internet of Things (IoT) networks, with a focus on minimizing the Age of Information (AoI) during data collection. Adopting a tutorial-style approach, it bridges theoretical models and practical algorithms for real-time decision-making in tasks like UAV trajectory planning, sensor transmission scheduling, and energy-efficient data gathering. Applications span precision agriculture, environmental monitoring, smart cities, and emergency response, showcasing the adaptability of DRL in UAV-based IoT systems. Designed as a foundational reference, it is ideal for researchers and engineers aiming to deepen their understanding of adaptive UAV planning across diverse IoT applications. This book offers a structured exploration of how Markov Decision Processes (MDPs) and Deep Reinforcement Learning (DRL) can be used to model and optimize UAV-assisted Internet of Things (IoT) networks, with a focus on minimizing the Age of Information (AoI) during data collection. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Bachelor Thesis from the year 2021 in the subject Engineering - Computer Engineering, grade: 1,3, Hamburg University of Technology (Embedded Systems), language: English, abstract: This bachelor thesis aims to illustrate the idea behind Markov Decision Processes (MDP) and to present a few basic methods of Reinforcement Learning (RL) namely Monte Carlo Learning and Q-Learning, which are the solutions for decision problems modelled by MDPs. For the last section we apply these methods on an application and in the end discuss the results.Let us imagine the scenario where we put a hamster inside a maze, we expect the hamster to go through the maze till it reaches some point we considered as the goal. Well, it may randomly work but most of the time it won't. At this place, the hamster does not know how important this particular point remains namely the goal. But how will it be, when we remunerate the hamster once the goal is reached, he receives a reward for example a piece of cheese. The hamster will start to remember the route, which leads to the cheese and he maybe will learn to go the easy and quick way to achieve this goal. What we did, is that we reinforce the good behavior of the hamster by giving it some reward. 32 pp. Englisch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - High Quality Content by WIKIPEDIA articles! Inspired by related psychological theory, in computer science, reinforcement learning is a sub-area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. Reinforcement learning algorithms attempt to find a policy that maps states of the world to the actions the agent ought to take in those states. In economics and game theory, reinforcement learning is considered as a boundedly rational interpretation of how equilibrium may arise.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - High Quality Content by WIKIPEDIA articles! Inspired by related psychological theory, in computer science, reinforcement learning is a sub-area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. Reinforcement learning algorithms attempt to find a policy that maps states of the world to the actions the agent ought to take in those states. In economics and game theory, reinforcement learning is considered as a boundedly rational interpretation of how equilibrium may arise. The environment is typically formulated as a finite-state Markov decision process (MDP), and reinforcement learning algorithms for this context are highly related to dynamic programming techniques. State transition probabilities and reward probabilities in the MDP are typically stochastic but stationary over the course of the problem.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Bachelor Thesis from the year 2021 in the subject Engineering - Computer Engineering, grade: 1,3, Hamburg University of Technology (Embedded Systems), language: English, abstract: This bachelor thesis aims to illustrate the idea behind Markov Decision Processes (MDP) and to present a few basic methods of Reinforcement Learning (RL) namely Monte Carlo Learning and Q-Learning, which are the solutions for decision problems modelled by MDPs. For the last section we apply these methods on an application and in the end discuss the results.Let us imagine the scenario where we put a hamster inside a maze, we expect the hamster to go through the maze till it reaches some point we considered as the goal. Well, it may randomly work but most of the time it won't. At this place, the hamster does not know how important this particular point remains namely the goal. But how will it be, when we remunerate the hamster once the goal is reached, he receives a reward for example a piece of cheese. The hamster will start to remember the route, which leads to the cheese and he maybe will learn to go the easy and quick way to achieve this goal. What we did, is that we reinforce the good behavior of the hamster by giving it some reward.GRIN Publishing GmbH, Waltherstraße 23, 80337 München 32 pp. Englisch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - High Quality Content by WIKIPEDIA articles! SARSA (State-Action-Reward-State-Action) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning. It was introduced in the technical note 'Online Q-Learning using Connectionist Systems' by Rummery & Niranjan (1994) where the alternative name SARSA was only mentioned as a footnote. This name simply reflects the fact that the main function for updating the Q-value depends on the current state of the agent 'S1', the action the agent chooses 'A1', the reward 'R' the agent gets for choosing this action, the state 'S2' that the agent will now be in after taking that action, and finally the next action 'A2' the agent will choose in its new state. Taking every letter in the quintuple (st , at , rt+1 , st+1 , at+1) yields the word SARSA.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Q-learning is a reinforcement learning technique that works by learning an action-value function that gives the expected utility of taking a given action in a given state and following a fixed policy thereafter. One of the strengths of Q-learning is that it is able to compare the expected utility of the available actions without requiring a model of the environment. A recent variation called delayed-Q learning has shown substantial improvements, bringing PAC bounds to Markov decision processes.…

Taschenbuch. Condition: Neu. Methods of Machine Learning and their Application. The Basics of Markov Decision Processes and Reinforcement Learning | Omar Baiazid | Taschenbuch | Englisch | 2021 | GRIN Verlag | EAN 9783346518194 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…

Taschenbuch. Condition: Neu. Reinforcement Learning | Computer Science, Machine Learning, Game Theory, Bounded Rationality, Markov Decision Process, Supervised Learning, Multi-Armed Bandit, Robot Control, Monte Carlo Method | Lambert M. Surhone (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786130348175 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…

Taschenbuch. Condition: Neu. Reinforcement Learning | Bounded Rationality, Markov Decision Process, Dynamic Programming, Supervised Learning, Training Let, Inductive Bias, Concept Learning, Handwriting Recognition | Lambert M. Surhone (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786130346560 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book offers a structured exploration of how Markov Decision Processes (MDPs) and Deep Reinforcement Learning (DRL) can be used to model and optimize UAV-assisted Internet of Things (IoT) networks, with a focus on minimizing the Age of Information (AoI) during data collection. Adopting a tutorial-style approach, it bridges theoretical models and practical algorithms for real-time decision-making in tasks like UAV trajectory planning, sensor transmission scheduling, and energy-efficient data gathering. Applications span precision agriculture, environmental monitoring, smart cities, and emergency response, showcasing the adaptability of DRL in UAV-based IoT systems. Designed as a foundational reference, it is ideal for researchers and engineers aiming to deepen their understanding of adaptive UAV planning across diverse IoT applications. 142 pp. Englisch.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book offers a structured exploration of how Markov Decision Processes (MDPs) and Deep Reinforcement Learning (DRL) can be used to model and optimize UAV-assisted Internet of Things (IoT) networks, with a focus on minimizing the Age of Information (AoI) during data collection. Adopting a tutorial-style approach, it bridges theoretical models and practical algorithms for real-time decision-making in tasks like UAV trajectory planning, sensor transmission scheduling, and energy-efficient data gathering. Applications span precision agriculture, environmental monitoring, smart cities, and emergency response, showcasing the adaptability of DRL in UAV-based IoT systems. Designed as a foundational reference, it is ideal for researchers and engineers aiming to deepen their understanding of adaptive UAV planning across diverse IoT applications.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 156 pp. Englisch.…