Reinforcement Learning Algorithms with Python [electronic resource] : Learn, Understand, and Develop Smart Algorithms for Addressing AI Challenges

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자료유형	e-Book
서명/저자사항	Reinforcement Learning Algorithms with Python [electronic resource]: Learn, Understand, and Develop Smart Algorithms for Addressing AI Challenges / Andrea Lonza.
개인저자	Lonza, Andrea.
발행사항	Birmingham: Packt Publishing, Limited, 2019.
형태사항	1 online resource (356 pages).
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, 짤2019 9781789131116
ISBN	1789139708 9781789139709
일반주기	Implementing REINFORCE with baseline
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)
분류기호(DDC)	005.1
언어	영어
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