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Foundations of deep reinforcement learning : theory and practice in Python

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자료유형 :	단행본
ISBN :	9780135172384
ISBN :	0135172381
분류기호 :	006.31
개인저자 :	Graesser, Laura, author.
서명/저자사항 :	Foundations of deep reinforcement learning : theory and practice in Python / Laura Graesser, Wah Loon Keng.
발행사항 :	Boston : Addison-Wesley, 2020.
형태사항 :	xxiv, 379 pages : color illustrations ; 23 cm.
총서사항 :	Addison Wesley data & analytics series
서지주기 :	Includes bibliographical references and index.
내용주기 :	1 Introduction to Reinforcement Learning -- 1.1 Reinforcement Learning -- 1.2 Reinforcement Learning as MDP -- 1.3 Learnable Functions in Reinforcement Learning -- 1.4 Deep Reinforcement Learning Algorithms -- 1.5 Deep Learning for Reinforcement Learning -- 1.6 Reinforcement Learning and Supervised Learning -- 1.6.1 Lack of an Oracle -- 1.6.2 Sparsity of Feedback -- 1.6.3 Data Generation -- I Policy-Based and Value-Based Algorithms --2 Reinforce -- 3 Sarsa -- 4 Deep Q-Networks (DQN) -- 5 Improving DQN -- II Combined Methods -- 6 Advantage Actor-Critic (A2C) -- 7 Proximal Policy Optimization (PPO) -- 8 Parallelization Methods -- 9 Algorithm Summary -- III Practical Details -- 10 Getting Deep RL to Work -- 11 SUM Lab -- 12 Network Architectures --13 Hardware -- IV Environment Design -- 14 States -- 15 Actions -- 16 Rewards -- 17 Transition Function -- A Deep Reinforcement Learning Timeline -- B Example Environments -- B.2 Continuous Environments
요약 :	The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games--such as Go, Atari games, and DotA 2--to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelized synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designed This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python.
일반주제명 :	Python (Computer program language) --
일반주제명 :	Reinforcement learning. --
일반주제명 :	Machine learning. --
일반주제명 :	Neural networks (Computer science) --
일반주제명 :	Artificial intelligence. --
일반주제명 :	Artificial intelligence. --
일반주제명 :	Machine learning. --
일반주제명 :	Neural networks (Computer science) --
일반주제명 :	Python (Computer program language) --
일반주제명 :	Reinforcement learning. --
개인저자 :	Keng, Wah Loon, author.
언어	영어