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Reinforcement Learning Algorithms with Python : Learn, Understand, and Develop Smart Algorithms for Addressing AI Challenges /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Lonza, Andrea. | |
| 245 | 1 | 0 | |a Reinforcement Learning Algorithms with Python : |b Learn, Understand, and Develop Smart Algorithms for Addressing AI Challenges / |c Andrea Lonza. |
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2019. | ||
| 300 | |a 1 online resource (356 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 500 | |a Implementing REINFORCE with baseline | ||
| 520 | |a 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. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Computer algorithms. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 2 | |a Algorithms | |
| 650 | 6 | |a Algorithmes. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a algorithms. |2 aat | |
| 650 | 7 | |a Computer algorithms. |2 fast |0 (OCoLC)fst00872010 | |
| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 776 | 0 | 8 | |i Print version: |a Lonza, Andrea. |t Reinforcement Learning Algorithms with Python : Learn, Understand, and Develop Smart Algorithms for Addressing AI Challenges. |d Birmingham : Packt Publishing, Limited, ©2019 |z 9781789131116 |
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