Aske Plaat's Books

Learning to Play

Reinforcement Learning and Games

By: Aske Plaat

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Learning to Play

Reinforcement Learning and Games

By: Aske Plaat

In this textbook the author takes as inspiration recent breakthroughs in game playing to explain how and why deep reinforcement learning works. In particular he shows why two-person games of tactics and strategy fascinate scientists, programmers, and game enthusiasts and unite them in a common goal: to create artificial intelligence (AI). After an introduction to the core concepts, environment, and communities of intelligence and games, the book is organized into chapters on reinforcement learning, heuristic planning, adaptive sampling, function approximation, and self-play. The author takes a hands-on approach throughout, with Python code examples and exercises that help the reader understand how AI learns to play. He also supports the main text with detailed pointers to online machine learning frameworks, technical details for AlphaGo, notes on how to play and program Go and chess, and a comprehensive bibliography. The content is class-tested and suitable for advanced undergraduate and graduate courses on artificial intelligence and games. It's also appropriate for self-study by professionals engaged with applications of machine learning and with games development. Finally it's valuable for any reader engaged with the philosophical implications of artificial and general intelligence, games represent a modern Turing test of the power and limitations of AI.

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Page Count

330

Publisher

Springer Nature

Published Date

ISBN 10

3030592383

ISBN 13

9783030592387

Deep Reinforcement Learning

By: Aske Plaat

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Deep Reinforcement Learning

By: Aske Plaat

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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Page Count

414

Publisher

Springer Nature

Published Date

ISBN 10

9811906386

ISBN 13

9789811906381

Learning to Play

Reinforcement Learning and Games

By: Aske Plaat

Write a review Read reviews Take a Quiz Solve Book Puzzle

Learning to Play

Reinforcement Learning and Games

By: Aske Plaat

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N/A

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Solve jigsaw puzzle

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Page Count

330

Publisher

Springer Nature

Published Date

ISBN 10

3030592383

ISBN 13

9783030592387

Deep Reinforcement Learning

By: Aske Plaat

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Deep Reinforcement Learning

By: Aske Plaat

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Solve jigsaw puzzle

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Page Count

414

Publisher

Springer Nature

Published Date

ISBN 10

9811906386

ISBN 13

9789811906381

Model-based Reinforcement Learning

A Survey

By: THOMAS M. MOERLAND; JOOST BROEKENS; ASKE PLAAT; CA.,Thomas M. Moerland,Joost Broekens,Aske Plaat,Catholijn M. Jonker

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Model-based Reinforcement Learning

A Survey

By: THOMAS M. MOERLAND; JOOST BROEKENS; ASKE PLAAT; CA.,Thomas M. Moerland,Joost Broekens,Aske Plaat,Catholijn M. Jonker

Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is an important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This monograph surveys an integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main steps: dynamics model learning and planning-learning integration. In this comprehensive survey of the topic, the authors first cover dynamics model learning, including challenges such as dealing with stochasticity, uncertainty, partial observability, and temporal abstraction. They then present a systematic categorization of planning-learning integration, including aspects such as: where to start planning, what budgets to allocate to planning and real data collection, how to plan, and how to integrate planning in the learning and acting loop.In conclusion the authors discuss implicit model-based RL as an end-to-end alternative for model learning and planning, and cover the potential benefits of model-based RL. Along the way, the authors draw connections to several related RL fields, including hierarchical RL and transfer learning. This monograph contains a broad conceptual overview of the combination of planning and learning for Markov Decision Process optimization. It provides a clear and complete introduction to the topic for students and researchers alike.

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1638280576

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Book's by Aske Plaat

Learning to Play

Learning to Play

Deep Reinforcement Learning

Deep Reinforcement Learning

Learning to Play

Learning to Play

Deep Reinforcement Learning

Deep Reinforcement Learning

Model-based Reinforcement Learning

Model-based Reinforcement Learning

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