자료유형 | E-Book |
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개인저자 | Lonza, Andrea. |
서명/저자사항 | Reinforcement Learning Algorithms with Python :Learn, Understand, and Develop Smart Algorithms for Addressing AI Challenges /Andrea Lonza. |
발행사항 | Birmingham : Packt Publishing, Limited, 2019. |
형태사항 | 1 online resource (356 pages) |
소장본 주기 | Added to collection customer.56279.3 |
ISBN | 1789139708 9781789139709 |
일반주기 |
Implementing REINFORCE with baseline
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내용주기 | Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: Algorithms and Environments; Chapter 1: The Landscape of Reinforcement Learning; An introduction to RL; Comparing RL and supervised learning; History of RL; Deep RL; Elements of RL; Policy; The value function; Reward; Model; Applications of RL; Games; Robotics and Industry 4.0; Machine learning; Economics and finance; Healthcare; Intelligent transportation systems; Energy optimization and smart grid; Summary; Questions; Further reading Chapter 2: Implementing RL Cycle and OpenAI GymSetting up the environment; Installing OpenAI Gym; Installing Roboschool; OpenAI Gym and RL cycles; Developing an RL cycle; Getting used to spaces; Development of ML models using TensorFlow; Tensor; Constant; Placeholder; Variable; Creating a graph; Simple linear regression example; Introducing TensorBoard; Types of RL environments; Why different environments?; Open source environments; Summary; Questions; Further reading; Chapter 3: Solving Problems with Dynamic Programming; MDP; Policy; Return; Value functions; Bellman equation Categorizing RL algorithmsModel-free algorithms; Value-based algorithms; Policy gradient algorithms; Actor-Critic algorithms; Hybrid algorithms; Model-based RL; Algorithm diversity; Dynamic programming; Policy evaluation and policy improvement; Policy iteration; Policy iteration applied to FrozenLake; Value iteration; Value iteration applied to FrozenLake; Summary; Questions; Further reading; Section 2: Model-Free RL Algorithms; Chapter 4: Q-Learning and SARSA Applications; Learning without a model; User experience; Policy evaluation; The exploration problem; Why explore?; How to explore TD learningTD update; Policy improvement; Comparing Monte Carlo and TD; SARSA; The algorithm; Applying SARSA to Taxi-v2; Q-learning; Theory; The algorithm; Applying Q-learning to Taxi-v2; Comparing SARSA and Q-learning; Summary; Questions; Chapter 5: Deep Q-Network; Deep neural networks and Q-learning; Function approximation; Q-learning with neural networks; Deep Q-learning instabilities; DQN; The solution; Replay memory; The target network; The DQN algorithm; The loss function; Pseudocode; Model architecture; DQN applied to Pong; Atari games; Preprocessing; DQN implementation; DNNs The experienced bufferThe computational graph and training loop; Results; DQN variations; Double DQN; DDQN implementation; Results; Dueling DQN; Dueling DQN implementation; Results; N-step DQN; Implementation; Results; Summary; Questions; Further reading; Chapter 6: Learning Stochastic and PG Optimization; Policy gradient methods; The gradient of the policy; Policy gradient theorem; Computing the gradient; The policy; On-policy PG; Understanding the REINFORCE algorithm; Implementing REINFORCE; Landing a spacecraft using REINFORCE; Analyzing the results; REINFORCE with baseline |
요약 | With this book, you will understand the core concepts and techniques of reinforcement learning. You will take a look into each RL algorithm and will develop your own self-learning algorithms and models. You will optimize the algorithms for better precision, use high-speed actions and lower the risk of anomalies in your applications. |
일반주제명 | Computer algorithms. Python (Computer program language) Computer algorithms. Python (Computer program language) |
언어 | 영어 |
기타형태 저록 | Print version:Lonza, Andrea.Reinforcement Learning Algorithms with Python : Learn, Understand, and Develop Smart Algorithms for Addressing AI Challenges.Birmingham : Packt Publishing, Limited, 짤20199781789131116 |
대출바로가기 | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2278656 |
인쇄
No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
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1 | WE00018222 | 005.1 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |
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